Code
library(tidyverse)
library(easystats)
library(patchwork)
library(ggside)
library(EGAnet)
library(tidygraph)
library(ggraph)
set.seed(42)
Sys.setenv(CHROMOTE_CHROME = "C:\\Program Files (x86)\\Microsoft\\Edge\\Application\\msedge.exe")library(tidyverse)
library(easystats)
library(patchwork)
library(ggside)
library(EGAnet)
library(tidygraph)
library(ggraph)
set.seed(42)
Sys.setenv(CHROMOTE_CHROME = "C:\\Program Files (x86)\\Microsoft\\Edge\\Application\\msedge.exe")df <- read.csv("../data/data_participants.csv") |>
rename(MINT_Urin_1 = MINT_Deficit_Urin_1,
MINT_Urin_2 = MINT_Deficit_Urin_2,
MINT_Urin_3 = MINT_Deficit_Urin_3,
MINT_CaCo_1 = MINT_Deficit_CaCo_4,
MINT_CaCo_2 = MINT_Deficit_CaCo_5,
MINT_CaCo_3 = MINT_Deficit_CaCo_6,
MINT_CaNo_1 = MINT_Deficit_CaNo_7,
MINT_CaNo_2 = MINT_Deficit_CaNo_8,
MINT_CaNo_3 = MINT_Deficit_CaNo_9,
MINT_Olfa_1 = MINT_Deficit_Olfa_10,
MINT_Olfa_2 = MINT_Deficit_Olfa_11,
MINT_Olfa_3 = MINT_Deficit_Olfa_12,
MINT_Sati_1 = MINT_Deficit_Sati_13,
MINT_Sati_2 = MINT_Deficit_Sati_14,
MINT_Sati_3 = MINT_Deficit_Sati_15,
MINT_SexS_1 = MINT_Awareness_SexS_19,
MINT_SexS_2 = MINT_Awareness_SexS_20,
MINT_SexS_3 = MINT_Awareness_SexS_21,
MINT_SexO_1 = MINT_Awareness_SexO_22,
MINT_SexO_2 = MINT_Awareness_SexO_23,
MINT_SexO_3 = MINT_Awareness_SexO_24,
MINT_UrSe_1 = MINT_Awareness_UrSe_25,
MINT_UrSe_2 = MINT_Awareness_UrSe_26,
MINT_UrSe_3 = MINT_Awareness_UrSe_27,
MINT_RelA_1 = MINT_Awareness_RelA_28,
MINT_RelA_2 = MINT_Awareness_RelA_29,
MINT_RelA_3 = MINT_Awareness_RelA_30,
MINT_StaS_1 = MINT_Awareness_StaS_31,
MINT_StaS_2 = MINT_Awareness_StaS_32,
MINT_StaS_3 = MINT_Awareness_StaS_33,
MINT_ExAc_1 = MINT_Awareness_ExAc_34,
MINT_ExAc_2 = MINT_Awareness_ExAc_35,
MINT_ExAc_3 = MINT_Awareness_ExAc_36,
MINT_Card_1 = MINT_Sensitivity_Card_37,
MINT_Card_2 = MINT_Sensitivity_Card_38,
MINT_Card_3 = MINT_Sensitivity_Card_39,
MINT_Resp_1 = MINT_Sensitivity_Resp_40,
MINT_Resp_2 = MINT_Sensitivity_Resp_41,
MINT_Resp_3 = MINT_Sensitivity_Resp_42,
MINT_Gast_1 = MINT_Sensitivity_Gast_46,
MINT_Gast_2 = MINT_Sensitivity_Gast_47,
MINT_Gast_3 = MINT_Sensitivity_Gast_48,
MINT_Derm_1 = MINT_Sensitivity_Derm_49,
MINT_Derm_2 = MINT_Sensitivity_Derm_50,
MINT_Derm_3 = MINT_Sensitivity_Derm_51)
colors <- c(Mint="#00BCD4", metaMint="#673AB7", miniMint="#26A36C", microMint="#8BC34A",
IAS="#F44336", MAIA="#FFAD04", iMAIA="#FF7811", BPQ="#795548")items <- select(df, starts_with("MINT_"))
names(items) <- str_remove(names(items), "MINT_")labels <- list(
MINT_Urin_1 = "I sometimes feel like I need to urinate or defecate but when I go to the bathroom I produce less than I expected",
MINT_Urin_2 = "I often feel the need to urinate even when my bladder is not full",
MINT_Urin_3 = "Sometimes I am not sure whether I need to go to the toilet or not (to urinate or defecate)",
MINT_CaCo_1 = "Sometimes my breathing becomes erratic or shallow and I often don't know why",
MINT_CaCo_2 = "I often feel like I can't get enough oxygen by breathing normally",
MINT_CaCo_3 = "Sometimes my heart starts racing and I often don't know why",
MINT_CaNo_1 = "I often only notice how I am breathing when it becomes loud",
MINT_CaNo_2 = "I only notice my heart when it is thumping in my chest",
MINT_CaNo_3 = "I often only notice how I am breathing when my breathing becomes shallow or irregular",
MINT_Olfa_1 = "I often check the smell of my armpits",
MINT_Olfa_2 = "I often check the smell of my own breath",
MINT_Olfa_3 = "I often check the smell of my farts",
MINT_Sati_1 = "I don't always feel the need to eat until I am really hungry",
MINT_Sati_2 = "Sometimes I don't realise I was hungry until I ate something",
MINT_Sati_3 = "I don't always feel the need to drink until I am really thirsty",
# MINT_SexA_16 = "I always feel in my body if I am sexually aroused",
# MINT_SexA_17 = "I can always tell that I am sexually aroused from the way I feel inside",
# MINT_SexA_18 = "I always know when I am sexually aroused",
MINT_SexS_1 = "During sex or masturbation, I often feel very strong sensations coming from my genital areas",
MINT_SexS_2 = "When I am sexually aroused, I often notice specific sensations in my genital area (e.g., tingling, warmth, wetness, stiffness, pulsations)",
MINT_SexS_3 = "My genital organs are very sensitive to pleasant stimulations",
MINT_SexO_1 = "In general, I am very sensitive to changes in my genital organs",
MINT_SexO_2 = "I can notice even very subtle changes in the state of my genital organs",
MINT_SexO_3 = "I am always very aware of the state of my genital organs, even when I am calm",
MINT_UrSe_1 = "In general, I am very aware of the sensations that are happening when I am urinating",
MINT_UrSe_2 = "In general, I am very aware of the sensations that are happening when I am defecating",
MINT_UrSe_3 = "I often experience a pleasant sensation when relieving myself when urinating or defecating)",
MINT_RelA_1 = "I always know when I am relaxed",
MINT_RelA_2 = "I always feel in my body if I am relaxed",
MINT_RelA_3 = "My body is always in the same specific state when I am relaxed",
MINT_StaS_1 = "Being relaxed is a very different bodily feeling compared to other states (e.g., feeling anxious, sexually aroused or after exercise)",
MINT_StaS_2 = "Being sexually aroused is a very different bodily feeling compared to other states (e.g., feeling anxious, relaxed, or after physical exercise)",
MINT_StaS_3 = "Being anxious is a very different bodily feeling compared to other states (e.g., feeling sexually aroused, relaxed or after exercise)",
MINT_ExAc_1 = "I can always accurately feel when I am about to burp",
MINT_ExAc_2 = "I can always accurately feel when I am about to fart",
MINT_ExAc_3 = "I can always accurately feel when I am about to sneeze",
MINT_Card_1 = "In general, I am very sensitive to changes in my heart rate",
MINT_Card_2 = "I can notice even very subtle changes in the way my heart beats",
MINT_Card_3 = "I often notice changes in my heart rate",
MINT_Resp_1 = "I can notice even very subtle changes in my breathing",
MINT_Resp_2 = "I am always very aware of how I am breathing, even when I am calm",
MINT_Resp_3 = "In general, I am very sensitive to changes in my breathing",
# MINT_Sign_43 = "When something important is happening in my life, I can feel immediately feel changes in my heart rate",
# MINT_Sign_44 = "When something important is happening in my life, I can immediately feel changes in my breathing",
# MINT_Sign_45 = "When something important is happening in my life, I can feel it in my body",
MINT_Gast_1 = "I can notice even very subtle changes in what my stomach is doing",
MINT_Gast_2 = "In general, I am very sensitive to what my stomach is doing",
MINT_Gast_3 = "I am always very aware of what my stomach is doing, even when I am calm",
MINT_Derm_1 = "In general, my skin is very sensitive",
MINT_Derm_2 = "My skin is susceptible to itchy fabrics and materials",
MINT_Derm_3 = "I can notice even very subtle stimulations to my skin (e.g., very light touches)"
# MINT_SexC_52 = "When I am sexually aroused, I often feel changes in the way my heart beats (e.g., faster or stronger)",
# MINT_SexC_53 = "When I am sexually aroused, I often feel changes in my breathing (e.g., faster, shallower, or less regular)",
# MINT_SexC_54 = "When I am sexually aroused, I often feel changes in my temperature (e.g., feeling warm or cold)"
)
cleanlabels <- function(x, qname=FALSE) {
is_factor <- FALSE
if(is.factor(x)) {
is_factor <- TRUE
vec <- x
x <- levels(x)
}
x <- x |>
str_replace("MINT_", ifelse(qname, "MINT - ", "")) |>
str_replace("MAIA_", ifelse(qname, "MAIA - ", "")) |>
str_replace("_", " - ")
if(is_factor) {
levels(vec) <- x
return(vec)
}
x
}ega1 <- bootEGA(
items,
EGA.type = "hierEGA",
model = "glasso", # BGGM
algorithm = "leiden",
allow.singleton = TRUE,
type="resampling",
plot.itemStability=FALSE,
typicalStructure=TRUE,
plot.typicalGraph=FALSE,
iter=500,
seed=3, ncores = 4)
save(ega1, file="models/ega1.RData")load("models/ega1.RData")
itemstability <- EGAnet::itemStability(ega1, IS.plot=FALSE)
plot(itemstability)
hclust <- pvclust::pvclust(items,
method.hclust = "ward.D2",
method.dist = "correlation",
nboot = 1000, quiet=TRUE, parallel=TRUE)
plot(hclust, hang = -1, cex = 0.5)
pvclust::pvrect(hclust, alpha=0.95, max.only=TRUE)
rbind(rez, grandave) |>
select(-Group, -Rank) |>
pivot_wider(names_from = "Outcome", values_from = "R2") |>
mutate(Group = case_when(
Dimension %in% c("Card", "Resp", "Gast", "CardRespGast", "CardResp", "CardGast") ~ "Visceral",
Dimension %in% c("RelA", "SexS", "ExaC", "StaS", "SexO", "UrSe") ~ "sensitivity",
Dimension %in% c("CaCo", "Urin", "Derm", "Olfa", "Sati", "CaNo") ~ "Deficit",
.default = "Other")) |>
select(Group, Dimension, Average, Interoception, Biofeedback, ER,
Primals, Alexithymia, Dissociative, Mood,
Physical, Mental, Lifestyle) |>
arrange(Group, desc(Average)) |>
# select(-Group) |>
gt::gt() |>
gt::data_color(
columns = -c(Dimension, Group),
palette = c("white", "green"),
domain = c(0, 1)
) |>
gt::fmt_number() |>
gt::tab_header(
title = "Variable Importance",
subtitle = "Normalized Predictive Power (R2)"
) |>
gt::tab_row_group(
label = "Sensitivity",
rows = Group == "sensitivity"
) |>
gt::tab_row_group(
label = "Interoception",
rows = Group == "Visceral"
) |>
gt::tab_row_group(
label = "Deficit",
rows = Group == "Deficit"
) |>
gt::cols_hide(Group)| Variable Importance | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Normalized Predictive Power (R2) | |||||||||||
| Dimension | Average | Interoception | Biofeedback | ER | Primals | Alexithymia | Dissociative | Mood | Physical | Mental | Lifestyle |
| Deficit | |||||||||||
| CaCo | 0.62 | 0.10 | 0.05 | 0.32 | 0.77 | 1.00 | 1.00 | 1.00 | 0.64 | 0.72 | 0.76 |
| Urin | 0.43 | 0.09 | 0.20 | 0.07 | 0.54 | 0.64 | 0.52 | 0.61 | 0.74 | 0.45 | 0.22 |
| Derm | 0.32 | 0.06 | 0.01 | 0.15 | 0.35 | 0.29 | 0.20 | 0.34 | 0.87 | 0.59 | 0.08 |
| Olfa | 0.25 | 0.09 | 0.42 | 0.22 | 0.46 | 0.29 | 0.16 | 0.22 | 0.03 | 0.20 | 0.00 |
| Sati | 0.24 | 0.01 | 0.01 | 0.10 | 0.31 | 0.56 | 0.36 | 0.20 | 0.06 | 0.23 | 0.53 |
| CaNo | 0.05 | 0.01 | 0.01 | 0.05 | 0.00 | 0.10 | 0.01 | 0.06 | 0.02 | 0.03 | 0.20 |
| Interoception | |||||||||||
| CardRespGast | 0.32 | 0.78 | 0.81 | 0.26 | 0.11 | 0.04 | 0.08 | 0.19 | 0.28 | 0.22 | 0.01 |
| CardResp | 0.31 | 0.70 | 0.80 | 0.19 | 0.08 | 0.05 | 0.09 | 0.21 | 0.20 | 0.13 | 0.01 |
| CardGast | 0.28 | 0.62 | 0.73 | 0.19 | 0.10 | 0.03 | 0.07 | 0.19 | 0.36 | 0.23 | 0.01 |
| Card | 0.28 | 0.48 | 0.67 | 0.06 | 0.07 | 0.05 | 0.09 | 0.23 | 0.22 | 0.13 | 0.00 |
| Resp | 0.25 | 0.70 | 0.62 | 0.26 | 0.07 | 0.04 | 0.06 | 0.13 | 0.10 | 0.15 | 0.03 |
| Gast | 0.17 | 0.43 | 0.26 | 0.17 | 0.08 | 0.00 | 0.02 | 0.06 | 0.12 | 0.07 | 0.03 |
| Sensitivity | |||||||||||
| RelA | 0.54 | 0.77 | 0.49 | 0.92 | 0.79 | 0.57 | 0.47 | 0.29 | 0.21 | 0.22 | 0.51 |
| SexS | 0.23 | 0.28 | 0.54 | 0.07 | 0.22 | 0.15 | 0.15 | 0.03 | 0.02 | 0.08 | 0.41 |
| UrSe | 0.21 | 0.36 | 0.66 | 0.40 | 0.14 | 0.01 | 0.00 | 0.00 | 0.03 | 0.02 | 0.07 |
| ExaC | 0.20 | 0.16 | 0.12 | 0.11 | 0.33 | 0.10 | 0.09 | 0.03 | 0.04 | 0.17 | 0.08 |
| SexO | 0.18 | 0.43 | 0.48 | 0.23 | 0.03 | 0.00 | 0.00 | 0.01 | 0.00 | 0.02 | 0.19 |
| StaS | 0.16 | 0.21 | 0.13 | 0.04 | 0.10 | 0.16 | 0.28 | 0.02 | 0.02 | 0.08 | 0.18 |
rbind(rez, grandave) |>
select(-Group, -R2) |>
pivot_wider(names_from = "Outcome", values_from = "Rank") |>
mutate(Group = case_when(
Dimension %in% c("Card", "Resp", "Gast", "CardRespGast", "CardResp", "CardGast") ~ "Visceral",
Dimension %in% c("RelA", "SexS", "ExaC", "StaS", "SexO", "UrSe") ~ "sensitivity",
Dimension %in% c("CaCo", "Urin", "Derm", "Olfa", "Sati", "CaNo") ~ "Deficit",
.default = "Other")) |>
datawizard::data_relocate(select=c("Group", "Dimension", "Average", "Interoception")) |>
arrange(Group, Average) |>
gt::gt() |>
# gt::fmt_number(columns = "Average") |>
gt::data_color(
columns = -c(Dimension, Group),
palette = c("green", "white"),
domain = c(1, 18)
) |>
gt::opt_interactive(page_size_default=20)hclust2 <- rez |>
filter(Group != Outcome) |>
select(-Group, -Rank) |>
pivot_wider(names_from = "Dimension", values_from = "R2") |>
select(-Outcome) |>
pvclust::pvclust(
method.hclust = "average",
method.dist = "correlation",
nboot = 1000, quiet=TRUE, parallel=TRUE)
plot(hclust2, hang = -1, cex = 0.5)
pvclust::pvrect(hclust2, alpha=0.95, max.only=TRUE)
items <- items |>
select(
-contains("UrSe"), # Unstable item
-contains("StaS"), # Too vague and overlapping
-contains("SexO"), # Overalapping with SexS
-contains("CaNo") # Overlap with CaCo
# -contains("Olfa"), # Unstable metacluster
# -contains("Sati")
)ega2 <- bootEGA(
items,
EGA.type = "hierEGA",
model = "glasso", # BGGM
algorithm = "leiden",
# consensus.method = "lowest_tefi",
allow.singleton = TRUE,
type="resampling",
plot.itemStability=FALSE,
typicalStructure=TRUE,
plot.typicalGraph=FALSE,
iter=500,
seed=3, ncores = 4)
save(ega2, file="models/ega2.RData")load("models/ega2.RData")
# EGAnet::dimensionStability(ega)
itemstability <- EGAnet::itemStability(ega2, IS.plot=FALSE)
p_stab <- plot(itemstability)
p_stabmake_loadingtable <- function(x) {
t <- as.data.frame(x) |>
datawizard::data_addprefix("C")
t$Cluster <- colnames(t)[max.col(t, ties.method='first')]
t |>
rownames_to_column(var="Item") |>
rowwise() |>
mutate(Max = max(c_across(-c(Item, Cluster)))) |>
arrange(Cluster, desc(Max)) |>
as.data.frame()
}
dimensions <- list(
C01 = "SexS", # Sexual Arousal Sensitivity
C02 = "Derm", # Dermal Hypersensitivity
C03 = "Card", # Cardioception
C04 = "Sati", # Satiety Awareness
C05 = "Gast", # Gastroception
C06 = "Olfa", # Olfactory Compensation
C07 = "Resp", # Respiroception
C08 = "RelA", # Relaxation Awareness
C09 = "Urin", # Urointestinal Inaccuracy
C10 = "ExAc", # Expulsion Accuracy
C11 = "CaCo" # Cardiorespiratory Confusion
# C01 = "C01",
# C02 = "C02",
# C03 = "C03",
# C04 = "C04",
# C05 = "C05",
# C06 = "C06",
# C07 = "C07",
# C08 = "C08",
# C09 = "C09",
# C10 = "C10",
# C11 = "C11",
# C12 = "C12"
)
higher <- make_loadingtable(t(net.loads(ega2$EGA$higher_order, loading.method="revised")$std)) |>
arrange(Item)
cluster_order <- names(higher)[!names(higher) %in% c("Item", "Cluster", "Max")]
lower <- make_loadingtable(net.loads(ega2$EGA$lower_order, loading.method="revised")$std) |>
select(names(higher)) |>
mutate(Cluster = fct_relevel(Cluster, cluster_order)) |>
arrange(Cluster, desc(Max))
dimensions <- dimensions[names(dimensions) %in% names(lower)]
higher$Item <- paste0("M", higher$Item)
# higher$Label <- c("Interoceptive Deficit", "Interoceptive Awareness", "Interoceptive Sensitivity")
higher$Label <- c("Interoceptive Awareness", "Interoceptive Deficit", "Visceroception")
higher$ID <- ""
lower <- mutate(lower, Label = labels[paste0("MINT_", Item)], ID=Item, Item = 1:n())
tab1 <- rbind(higher, lower) |>
select(-ID) |>
datawizard::data_relocate("Label", after = "Item") |>
datawizard::data_rename(names(dimensions), dimensions[names(dimensions)]) |>
select(-Cluster, -Max) |>
mutate(`|` = "") |>
gt::gt() |>
gt::tab_row_group(
label = "Items",
rows = 1:(nrow(lower) + nrow(higher))
) |>
gt::tab_row_group(
label = "Metaclusters",
rows = 1:nrow(higher)
) |>
gt::data_color(columns=-Item,
method = "numeric",
palette = c("red", "white", "green"),
domain = c(-1, 1)) |>
gt::tab_style(
style = gt::cell_text(size="small", style="italic"),
locations = gt::cells_body(columns="Label", rows=c(4:36))
) |>
gt::tab_style(
style = list(gt::cell_text(weight="bold"),
gt::cell_fill(color = "#F5F5F5")),
locations = list(
gt::cells_row_groups(groups = "Items"),
gt::cells_row_groups(groups = "Metaclusters")
)
) |>
gt::fmt_number(columns=-Item, decimals=2) |>
gt::tab_header(
title = gt::md("**Item Loadings**"),
subtitle = "Node centrality"
) |>
# Metacluster 1
gt::tab_style(
style = gt::cell_fill(color = "#81C784"),
locations = list(
gt::cells_body(columns="Item", rows=c(1))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#81C784"),
locations = list(
gt::cells_column_labels(columns = 3),
gt::cells_body(columns="Item", rows=c(4:6))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#AED581"),
locations = list(
gt::cells_column_labels(columns = 4),
gt::cells_body(columns="Item", rows=c(7:9))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#DCE775"),
locations = list(
gt::cells_column_labels(columns = 5),
gt::cells_body(columns="Item", rows=c(10:12))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#BA68C8"),
locations = list(
gt::cells_body(columns="Item", rows=c(2))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#BA68C8"),
locations = list(
gt::cells_column_labels(columns = 6),
gt::cells_body(columns="Item", rows=c(13:15))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#9575CD"),
locations = list(
gt::cells_column_labels(columns = 7),
gt::cells_body(columns="Item", rows=c(16:18))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#7986CB"),
locations = list(
gt::cells_column_labels(columns = 8),
gt::cells_body(columns="Item", rows=c(19:21))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#64B5F6"),
locations = list(
gt::cells_column_labels(columns = 9),
gt::cells_body(columns="Item", rows=c(22:24))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#4DD0E1"),
locations = list(
gt::cells_column_labels(columns = 10),
gt::cells_body(columns="Item", rows=c(25:27))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#FF7811"),
locations = list(
gt::cells_body(columns="Item", rows=c(3))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#FFB74D"),
locations = list(
gt::cells_column_labels(columns = 11),
gt::cells_body(columns="Item", rows=c(28:30))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#E57373"),
locations = list(
gt::cells_column_labels(columns = 12),
gt::cells_body(columns="Item", rows=c(31:33))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#A1887F"),
locations = list(
gt::cells_column_labels(columns = 13),
gt::cells_body(columns="Item", rows=c(34:36))
)
)
tab1| Item Loadings | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Node centrality | |||||||||||||
| Item | Label | ExAc | RelA | SexS | CaCo | Urin | Derm | Sati | Olfa | Resp | Card | Gast | | |
| Metaclusters | |||||||||||||
| M1 | Interoceptive Awareness | 0.42 | 0.41 | 0.30 | −0.20 | −0.06 | 0.06 | −0.07 | 0.08 | 0.13 | 0.02 | 0.12 | |
| M2 | Interoceptive Deficit | 0.00 | −0.17 | 0.01 | 0.49 | 0.44 | 0.24 | 0.23 | 0.18 | 0.21 | 0.20 | 0.12 | |
| M3 | Visceroception | 0.02 | 0.15 | 0.02 | 0.20 | 0.01 | 0.11 | 0.00 | 0.13 | 0.60 | 0.58 | 0.33 | |
| Items | |||||||||||||
| 1 | I can always accurately feel when I am about to fart | 0.48 | 0.02 | 0.05 | 0.00 | 0.00 | 0.04 | −0.01 | 0.10 | 0.00 | 0.00 | 0.00 | |
| 2 | I can always accurately feel when I am about to sneeze | 0.48 | 0.09 | 0.01 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
| 3 | I can always accurately feel when I am about to burp | 0.46 | 0.06 | 0.01 | −0.02 | −0.04 | 0.05 | −0.06 | 0.00 | 0.03 | 0.00 | 0.00 | |
| 4 | I always feel in my body if I am relaxed | 0.02 | 0.59 | 0.02 | −0.04 | 0.00 | 0.00 | −0.01 | 0.00 | 0.04 | 0.00 | 0.03 | |
| 5 | I always know when I am relaxed | 0.10 | 0.58 | 0.03 | −0.10 | −0.07 | 0.00 | −0.03 | −0.01 | 0.03 | 0.03 | 0.02 | |
| 6 | My body is always in the same specific state when I am relaxed | 0.04 | 0.28 | 0.00 | −0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | 0.00 | |
| 7 | During sex or masturbation, I often feel very strong sensations coming from my genital areas | 0.01 | 0.03 | 0.65 | −0.05 | −0.04 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
| 8 | My genital organs are very sensitive to pleasant stimulations | 0.00 | 0.02 | 0.53 | 0.00 | 0.00 | 0.02 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | |
| 9 | When I am sexually aroused, I often notice specific sensations in my genital area (e.g., tingling, warmth, wetness, stiffness, pulsations) | 0.08 | 0.02 | 0.53 | 0.00 | 0.01 | 0.03 | 0.00 | 0.02 | 0.00 | 0.01 | 0.02 | |
| 10 | Sometimes my breathing becomes erratic or shallow and I often don't know why | −0.02 | −0.01 | 0.00 | 0.68 | 0.11 | 0.00 | 0.09 | 0.02 | 0.07 | 0.00 | 0.00 | |
| 11 | I often feel like I can't get enough oxygen by breathing normally | 0.00 | −0.07 | −0.04 | 0.39 | 0.04 | 0.04 | 0.02 | 0.02 | 0.06 | 0.04 | 0.00 | |
| 12 | Sometimes my heart starts racing and I often don't know why | 0.00 | −0.07 | 0.00 | 0.37 | 0.06 | 0.05 | 0.03 | 0.00 | 0.00 | 0.16 | 0.00 | |
| 13 | I sometimes feel like I need to urinate or defecate but when I go to the bathroom I produce less than I expected | 0.00 | 0.00 | 0.01 | 0.08 | 0.63 | 0.03 | 0.01 | 0.07 | 0.00 | 0.00 | 0.00 | |
| 14 | I often feel the need to urinate even when my bladder is not full | 0.00 | −0.01 | 0.00 | 0.04 | 0.44 | 0.09 | 0.00 | 0.02 | 0.00 | 0.00 | 0.01 | |
| 15 | Sometimes I am not sure whether I need to go to the toilet or not (to urinate or defecate) | −0.04 | −0.06 | −0.03 | 0.09 | 0.32 | 0.00 | 0.12 | 0.00 | 0.00 | 0.00 | 0.00 | |
| 16 | In general, my skin is very sensitive | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.70 | 0.02 | 0.00 | 0.04 | 0.00 | 0.01 | |
| 17 | My skin is susceptible to itchy fabrics and materials | 0.00 | 0.00 | 0.00 | 0.07 | 0.07 | 0.46 | 0.01 | 0.01 | 0.00 | 0.00 | 0.01 | |
| 18 | I can notice even very subtle stimulations to my skin (e.g., very light touches) | 0.13 | 0.00 | 0.04 | 0.00 | 0.05 | 0.30 | 0.00 | 0.00 | 0.02 | 0.03 | 0.04 | |
| 19 | I don't always feel the need to eat until I am really hungry | −0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.60 | 0.00 | 0.00 | 0.00 | 0.00 | |
| 20 | Sometimes I don't realise I was hungry until I ate something | −0.05 | −0.02 | 0.00 | 0.09 | 0.10 | 0.01 | 0.49 | 0.02 | 0.01 | 0.00 | 0.00 | |
| 21 | I don't always feel the need to drink until I am really thirsty | 0.00 | −0.03 | 0.00 | 0.04 | 0.03 | 0.00 | 0.23 | 0.03 | 0.00 | −0.01 | 0.00 | |
| 22 | I often check the smell of my armpits | 0.00 | −0.01 | 0.00 | 0.02 | 0.03 | 0.00 | 0.04 | 0.59 | 0.04 | 0.03 | 0.00 | |
| 23 | I often check the smell of my own breath | 0.00 | 0.00 | 0.01 | 0.02 | 0.00 | 0.00 | 0.02 | 0.55 | 0.04 | 0.00 | 0.04 | |
| 24 | I often check the smell of my farts | 0.10 | 0.00 | 0.01 | 0.00 | 0.06 | 0.01 | 0.00 | 0.28 | 0.00 | 0.00 | 0.01 | |
| 25 | In general, I am very sensitive to changes in my breathing | 0.00 | 0.04 | 0.00 | 0.06 | 0.00 | 0.07 | 0.00 | 0.02 | 0.54 | 0.15 | 0.02 | |
| 26 | I can notice even very subtle changes in my breathing | 0.03 | 0.03 | 0.00 | 0.03 | 0.00 | 0.00 | 0.00 | 0.01 | 0.54 | 0.14 | 0.02 | |
| 27 | I am always very aware of how I am breathing, even when I am calm | 0.00 | 0.02 | 0.00 | 0.04 | 0.00 | 0.00 | 0.01 | 0.04 | 0.43 | 0.05 | 0.06 | |
| 28 | In general, I am very sensitive to changes in my heart rate | 0.00 | 0.00 | 0.00 | 0.07 | 0.00 | 0.03 | −0.01 | 0.00 | 0.11 | 0.55 | 0.03 | |
| 29 | I often notice changes in my heart rate | 0.00 | 0.00 | 0.01 | 0.14 | 0.00 | 0.00 | 0.00 | 0.03 | 0.08 | 0.53 | 0.01 | |
| 30 | I can notice even very subtle changes in the way my heart beats | 0.00 | 0.07 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.17 | 0.45 | 0.05 | |
| 31 | I can notice even very subtle changes in what my stomach is doing | 0.00 | 0.02 | 0.00 | 0.00 | 0.01 | 0.04 | 0.00 | 0.03 | 0.05 | 0.04 | 0.59 | |
| 32 | In general, I am very sensitive to what my stomach is doing | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.02 | 0.00 | 0.02 | 0.00 | 0.02 | 0.58 | |
| 33 | I am always very aware of what my stomach is doing, even when I am calm | 0.00 | 0.04 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.07 | 0.04 | 0.53 | |
gt::gtsave(tab1, "figures/table1.png", vwidth=1660, expand=0, quiet=TRUE)rez <- pvclust::pvclust(items,
method.hclust = "ward.D2",
method.dist = "correlation",
nboot = 1000, quiet=TRUE, parallel=TRUE)
plot(rez, hang = -1, cex = 0.5)
pvclust::pvrect(rez, alpha=0.95, max.only=FALSE)
dendrogram <- as.dist(1 - cor(items, use = "pairwise.complete.obs")) |>
hclust(method = "ward.D2") |>
tidygraph::as_tbl_graph()
# Process Nodes
nodes <- as.list(dendrogram)$nodes |>
mutate(Group = case_when(str_detect(label, "Card") ~ "Card",
str_detect(label, "Resp") ~ "Resp",
str_detect(label, "Gast") ~ "Gast",
str_detect(label, "SexS") ~ "SexS",
str_detect(label, "RelA") ~ "RelA",
str_detect(label, "ExAc") ~ "ExAc",
str_detect(label, "Urin") ~ "Urin",
str_detect(label, "CaCo") ~ "CaCo",
str_detect(label, "Derm") ~ "Derm",
str_detect(label, "Olfa") ~ "Olfa",
str_detect(label, "Sati") ~ "Sati",
label == "" ~ "NA",
.default = "Other"),
Size = setNames(lower$Max, lower$ID)[label],
item = label)
nodes$idx <- 1:nrow(nodes)
# Rename accoring to table
items_newnames <- lower |>
separate(ID, into = c("Group", "Item"), sep = "_", remove = FALSE) |>
mutate(Name = paste0(Group, "-", 1:nrow(lower)))
items_newnames <- setNames(items_newnames$Name, items_newnames$ID)
nodes$label <- items_newnames[nodes$label]
nodes$label <- ifelse(is.na(nodes$label), "", nodes$label)
# Central node
max_size <- max(nodes$Size, na.rm = TRUE)
nodes[nodes$height == max(nodes$height), c("Group", "Size")] <- data.frame(Group="Central", Size=2 * max_size)
# Metaclusters
nodes[17, c("Group", "Size")] <- data.frame(Group="Awareness", Size=1.5 * max_size)
nodes[34, c("Group", "Size")] <- data.frame(Group="Visceroception", Size=1.5 * max_size)
nodes[63, c("Group", "Size")] <- data.frame(Group="Deficit", Size=1.5 * max_size)
# Process Edges
edges <- as.list(dendrogram)$edges
edges$linewidth = datawizard::rescale(nodes[edges$from, ]$height, to = c(0.1, 1))
p_hclust <- tbl_graph(nodes = nodes, edges = edges) |>
ggraph(layout = "dendrogram", circular = TRUE) +
# geom_edge_diagonal(strength = 0.7, linewidth = 1) +
geom_edge_elbow2(aes(edge_width=linewidth), color="darkgrey") +
geom_node_point(aes(filter=Group %in% c("Central"), size = Size), color="darkgrey") +
geom_node_point(aes(filter=Group %in% c("Visceroception", "Awareness", "Deficit"), color=Group, size = Size)) +
geom_node_text(aes(
label = ifelse(label != "NA", label, NA),
x = x * 1.05,
y = y * 1.05,
filter = label != "",
angle = ifelse(
x >= 0,
asin(y) * 360 / 2 / pi,
360 - asin(y) * 360 / 2 / pi
),
hjust = ifelse(
x >= 0, 0, 1
))) +
geom_node_point(aes(filter = label != "", color=Group, size=Size), alpha = 1) +
# geom_node_text(aes(label=idx)) + # Debug
scale_edge_width_continuous(range=c(1, 3), guide = "none") +
scale_size_continuous(range=c(3, 11), guide = "none") +
scale_color_manual(values = c(
"Visceroception" = "#FF7811", "Card" = "#F44336", "Resp"="#FF9800", "Gast"="#795548",
"Awareness" = "#4CAF50", "ExAc"="#4CAF50", "RelA"="#8BC34A", "SexS" = "#CDDC39",
"Deficit" = "#9C27B0", "CaCo"="#9C27B0", "Urin" = "#673AB7", "Derm"="#3F51B5",
"Sati" = "#2196F3", "Olfa" = "#00BCD4"),
breaks = c(
"Awareness", "ExAc", "RelA", "SexS",
"Deficit", "CaCo", "Urin", "Derm", "Sati", "Olfa",
"Visceroception", "Resp", "Card", "Gast"
),
labels=c(
"***Awareness***", "ExAc", "RelA", "SexS",
"***Deficit***", "CaCo", "Urin", "Derm", "Sati", "Olfa",
"***Visceroception***", "Resp", "Card", "Gast"
)) +
ggtitle("Hierarchical Clustering", subtitle = "Method = Correlation") +
coord_equal(clip = "off", xlim = c(-1.25, 1.25), ylim = c(-1.25, 1.25)) +
theme_void() +
guides(color = guide_legend(override.aes = list(size = c(7.5, 5, 5, 3.5, 7.5, 5, 5, 3.5, 3.5, 3.5, 7.5, 5, 5, 3.5)))) +
theme(legend.text = ggtext::element_markdown(),
legend.title = element_blank(),
plot.title = element_blank(), #element_text(face="bold")
plot.subtitle = element_blank())
p_hclust
ggsave("figures/fig1a.png", p_stab, width=10, height=10*0.8, dpi=300)
ggsave("figures/fig1b.png", p_hclust, width=12, height=8, dpi=300)n <- parameters::n_factors(items)
plot(n)
fa <- parameters::factor_analysis(items, n = 9, rotation = "varimax", sort = TRUE)
plot(fa)
# fa |>
# select(Variable, Uniqueness) |>
# mutate(Group = str_extract(Variable, "^[A-Z][a-z]+")) |>
# mutate(Average = mean(Uniqueness), .by = "Group") |>
# arrange(desc(Average), desc(Uniqueness)) |>
# select(-Average, -Group) |>
# gt::gt() |>
# gt::data_color(
# columns = "Uniqueness",
# palette = c("red", "white", "green"),
# domain = c(0, 1)
# ) # Awareness
df$MINT_SexS <- rowMeans(select(df, starts_with("MINT_SexS_")), na.rm = TRUE)
df$MINT_ExaC <- rowMeans(select(df, starts_with("MINT_ExaC_")), na.rm = TRUE)
df$MINT_RelA <- rowMeans(select(df, starts_with("MINT_RelA_")), na.rm = TRUE)
# df$MINT_StaS <- rowMeans(select(df, starts_with("MINT_StaS_")), na.rm = TRUE)
# df$MINT_SexO <- rowMeans(select(df, starts_with("MINT_SexO_")), na.rm = TRUE)
# df$MINT_UrSe <- rowMeans(select(df, starts_with("MINT_UrSe_")), na.rm = TRUE)
# Deficit
df$MINT_Urin <- rowMeans(select(df, starts_with("MINT_Urin_")), na.rm = TRUE)
df$MINT_CaCo <- rowMeans(select(df, starts_with("MINT_CaCo_")), na.rm = TRUE)
df$MINT_Derm <- rowMeans(select(df, starts_with("MINT_Derm_")), na.rm = TRUE)
# df$MINT_CaNo <- rowMeans(select(df, starts_with("MINT_CaNo_")), na.rm = TRUE)
df$MINT_Sati <- rowMeans(select(df, starts_with("MINT_Sati_")), na.rm = TRUE)
df$MINT_Olfa <- rowMeans(select(df, starts_with("MINT_Olfa_")), na.rm = TRUE)
# Visceral
df$MINT_Resp <- rowMeans(select(df, starts_with("MINT_Resp_")), na.rm = TRUE)
df$MINT_Card <- rowMeans(select(df, starts_with("MINT_Card_")), na.rm = TRUE)
df$MINT_Gast <- rowMeans(select(df, starts_with("MINT_Gast_")), na.rm = TRUE)
df <- df[names(df)[!grepl("MINT_.*[0-9]$", names(df))]]mint <- select(df, starts_with("MINT_"))
metamint <- data.frame(
MINT_Awareness = rowMeans(select(df, MINT_SexS, MINT_ExaC, MINT_RelA)),
MINT_Deficit = rowMeans(select(df, MINT_CaCo, MINT_Urin, MINT_Derm, MINT_Sati, MINT_Olfa)),
MINT_Visceroception = rowMeans(select(df, MINT_Card, MINT_Resp, MINT_Gast))
)
minimint <- data.frame(
MINT_Awareness = rowMeans(select(df, MINT_ExaC, MINT_RelA)),
MINT_Deficit = rowMeans(select(df, MINT_CaCo, MINT_Urin)),
MINT_Visceroception = rowMeans(select(df, MINT_Resp, MINT_Card))
)
micromint <- data.frame(
MINT_ExaC = df$MINT_ExaC,
MINT_CaCo = df$MINT_CaCo,
MINT_Resp = df$MINT_Resp
)make_cor <- function(df1, df2=NULL, qname_x=TRUE, qname_y=TRUE, textsize = 2.9, xcol = "#424242", ycol="#424242") {
r <- df1 |>
correlation::correlation(data2=df2, p_adjust="none", redundant=ifelse(is.null(df2), TRUE, FALSE)) |>
correlation::cor_sort() |>
mutate(label = ifelse(p < .001, paste0(insight::format_value(r)), ""),
Parameter1 = cleanlabels(Parameter1, qname=qname_x),
Parameter2 = cleanlabels(Parameter2, qname=qname_y))
r[as.character(r$Parameter1) == as.character(r$Parameter2), "label"] <- ""
xnames <- levels(r$Parameter1)
ynames <- levels(r$Parameter2)
xcol <- case_when(
str_detect(xnames, "MINT") ~ "#00838F",
str_detect(xnames, "MAIA") ~ "#EF6C00",
str_detect(xnames, "IAS") ~ "#C62828",
str_detect(xnames, "BPQ") ~ "#795548",
.default = xcol
)
ycol <- case_when(
str_detect(ynames, "MINT") ~ "#00838F",
str_detect(ynames, "MAIA") ~ "#EF6C00",
str_detect(ynames, "IAS") ~ "#C62828",
str_detect(ynames, "BPQ") ~ "#795548",
.default = ycol
)
xbold <- case_when(
xnames %in% c("Awareness", "MINT - Awareness") ~ "bold",
str_detect(xnames, "Visceroception") ~ "bold",
str_detect(xnames, "Deficit") ~ "bold",
.default = "plain"
)
ybold <- case_when(
ynames %in% c("Awareness", "MINT - Awareness") ~ "bold",
str_detect(ynames, "Visceroception") ~ "bold",
str_detect(ynames, "Deficit") ~ "bold",
.default = "plain"
)
r |>
ggplot(aes(x=Parameter1, y=Parameter2, fill=r)) +
geom_tile() +
geom_text(aes(label=label), size=textsize) +
scale_fill_gradient2(low="blue", high="red", mid="white", midpoint=0) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust=1, color = xcol, face = xbold),
axis.text.y = element_text(color = ycol, face = ybold),
legend.position = "none",
axis.title = element_blank())
}
p_cormint <- make_cor(cbind(mint, metamint), qname_x = FALSE, qname_y = FALSE, textsize = 2.5,
xcol = "#00838F", ycol = "#00838F") +
labs(title="Correlations between MINT dimensions") +
theme(axis.title = element_blank())
p_cormint
p_corintero <- make_cor(select(df, starts_with("MAIA"), IAS, BPQ),
cbind(mint, metamint), qname_y = FALSE, textsize = 2.5,
ycol = "#00838F") +
labs(title="MINT vs. Other Interoception Questionnaires") +
theme(axis.title = element_blank())
p_corintero
Most correlated dimensions: - IAS: ExAc - Trusting: RelA - Noticing: Resp - Emotional Awareness: Resp - Body Listening: Gast - SelfRegulation: RelA - AttentionRegulation: RelA - BPQ: Card
table_models <- function(models) {
test <- test_performance(models)
table <- compare_performance(models, metrics=c("R2", "BIC"))
test$Model <- NULL
test$Omega2 <- NULL
test$p_Omega2 <- NULL
test$R2 <- table$R2
test$BIC <- table$BIC
preds <- c()
coefs <- c()
for(m in models){
x <- names(sort(abs(coef(m)[-1]), decreasing=TRUE))
preds <- c(preds, paste0(cleanlabels(x), collapse = ", "))
if(length(coef(m)) == 2) {
coefs <- c(coefs, coef(m)[2])
}
}
if(length(coefs) == length(models)) test$Coefficient <- coefs
format(test, zap_small = TRUE)
}
# Individual predictors ------------------------------------------------------
compare_predictors <- function(outcome="TAS_DIF", family = "linear") {
preds <- c("BPQ", "IAS",
names(select(df, starts_with("MINT"), -matches("[[:digit:]]"))),
names(select(df, starts_with("MAIA"))),
names(metamint))
models <- list()
for(p in preds) {
if(length(unique(df[[outcome]])) > 2) {
if (family == "linear") {
models[[p]] <- lm(as.formula(paste0(outcome, " ~ ", p)), data=cbind(df, metamint))
} else {
models[[p]] <- glm(as.formula(paste0(outcome, " ~ ", p)), data=cbind(df, metamint), family=family)
}
} else {
models[[p]] <- glm(as.formula(paste0(outcome, " ~ ", p)), data=standardize(cbind(df, metamint), exclude=outcome), family="binomial")
}
}
perf <- compare_performance(models) |>
arrange(AIC)
best_mint <- perf$Name[str_detect(perf$Name, "MINT_")][1]
best_maia <- perf$Name[str_detect(perf$Name, "MAIA")][1]
table <- table_models(models[c(best_mint, best_maia, "BPQ", "IAS")])
# Make plots --------------------------------------------------
df_pred <- data.frame()
for(p in c(best_mint, best_maia, "BPQ", "IAS")) {
pred <- modelbased::estimate_relation(models[[p]], by=p, length=30)
names(pred)[1] <- "Value"
pred$Predictor <- p
df_pred <- rbind(df_pred, pred)
}
cols <- colors
cols[[best_mint]] <- cols["Mint"]
cols[[best_maia]] <- cols["MAIA"]
p <- df_pred |>
mutate(Predictor = fct_rev(Predictor)) |>
ggplot(aes(x=Value, y=Predicted)) +
geom_ribbon(aes(ymin=CI_low, ymax=CI_high, fill=Predictor), alpha=0.3) +
geom_line(aes(color=Predictor)) +
scale_fill_manual(values=cols) +
scale_color_manual(values=cols) +
facet_wrap(~Predictor, scales="free_x") +
labs(y=outcome, x="Interoception Index")
list(table=table, p=p, outcome=outcome)
}
# Full Models ------------------------------------------------------
compare_models_lm <- function(outcome="TAS_DIF") {
# Full Questionnaires
m_mint <- lm(as.formula(paste0(outcome, " ~ .")),
data = cbind(mint, df[outcome]))
m_metamint <- lm(as.formula(paste0(outcome, " ~ .")),
data = cbind(metamint, df[outcome]))
m_minimint <- lm(as.formula(paste0(outcome, " ~ .")),
data = cbind(minimint, df[outcome]))
m_micromint <- lm(as.formula(paste0(outcome, " ~ .")),
data = cbind(micromint, df[outcome]))
m_ias <- lm(as.formula(paste0(outcome, " ~ IAS")),
data = df)
m_maia <- lm(as.formula(paste0(outcome, " ~ .")),
data = select(df, all_of(outcome), starts_with("MAIA")))
m_imaia <- lm(as.formula(paste0(outcome, " ~ .")),
data = select(df, all_of(outcome),
starts_with("MAIA"),
-contains("NotWorrying"),
-contains("NotDistracting")))
m_imaia <- lm(as.formula(paste0(outcome, " ~ .")),
data = select(df, all_of(outcome),
# MAIA_AttentionRegulation, # remove?
# MAIA_SelfRegulation, # remove?
# MAIA_Trusting, # remove?
MAIA_BodyListening,
MAIA_EmotionalAwareness,
MAIA_Noticing))
m_bpq <- lm(as.formula(paste0(outcome, " ~ BPQ")),
data = df)
models_full <- list(m_mint=m_mint, m_metamint=m_metamint,
m_minimint=m_minimint, m_micromint=m_micromint,
m_maia=m_maia, m_imaia=m_imaia,
m_ias=m_ias, m_bpq=m_bpq)
table_models(models_full)
}preds_dif <- compare_predictors("TAS_DIF")
display(preds_dif$table, title="DIF")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.22 | 1108.37 | 0.31 | |||
| MAIA_Trusting | < 0.001 | 1.33 | 0.091 | 0.17 | 1150.81 | -0.18 |
| BPQ | < 0.001 | 7.14 | < .001 | 0.01 | 1286.24 | 0.05 |
| IAS | < 0.001 | 3.81 | < .001 | 0.08 | 1226.64 | -0.02 |
Each model is compared to MINT_Deficit.
preds_dif$p
mods_dif <- compare_models_lm("TAS_DIF")
display(mods_dif, title="DIF")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.31 | 1081.51 | |||
| m_metamint | > 1000 | 2.53 | 0.006 | 0.29 | 1052.69 |
| m_minimint | < 0.001 | 4.25 | < .001 | 0.24 | 1104.97 |
| m_micromint | < 0.001 | 5.41 | < .001 | 0.19 | 1150.47 |
| m_maia | < 0.001 | 2.74 | 0.003 | 0.23 | 1148.37 |
| m_imaia | < 0.001 | 8.47 | < .001 | 0.02 | 1292.91 |
| m_ias | < 0.001 | 6.77 | < .001 | 0.08 | 1226.64 |
| m_bpq | < 0.001 | 8.89 | < .001 | 0.01 | 1286.24 |
Each model is compared to m_mint.
preds_ddf <- compare_predictors("TAS_DDF")
display(preds_ddf$table, title="DDF")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.18 | 1489.14 | 0.35 | |||
| MAIA_Trusting | 0.003 | 0.37 | 0.356 | 0.17 | 1500.71 | -0.22 |
| BPQ | < 0.001 | 5.99 | < .001 | 0.01 | 1629.83 | 0.07 |
| IAS | < 0.001 | 2.79 | 0.003 | 0.08 | 1571.78 | -0.02 |
Each model is compared to MINT_Deficit.
preds_ddf$p
mods_ddf <- compare_models_lm("TAS_DDF")
display(mods_ddf, title="DDF")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.25 | 1485.21 | |||
| m_metamint | > 1000 | 1.51 | 0.065 | 0.24 | 1441.45 |
| m_minimint | 0.045 | 3.75 | < .001 | 0.19 | 1491.40 |
| m_micromint | < 0.001 | 4.78 | < .001 | 0.15 | 1528.48 |
| m_maia | < 0.001 | 1.31 | 0.095 | 0.22 | 1502.42 |
| m_imaia | < 0.001 | 7.45 | < .001 | 0.01 | 1640.19 |
| m_ias | < 0.001 | 5.26 | < .001 | 0.08 | 1571.78 |
| m_bpq | < 0.001 | 7.69 | < .001 | 0.01 | 1629.83 |
Each model is compared to m_mint.
preds_eot <- compare_predictors("TAS_EOT")
display(preds_eot$table, title="EOT")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.20 | 931.21 | 0.25 | |||
| MAIA_Trusting | < 0.001 | 3.81 | < .001 | 0.07 | 1034.62 | -0.10 |
| BPQ | < 0.001 | 6.29 | < .001 | 0.01 | 1080.86 | 0.07 |
| IAS | < 0.001 | 4.78 | < .001 | 0.04 | 1061.30 | -0.01 |
Each model is compared to MINT_Deficit.
preds_eot$p
mods_eot <- compare_models_lm("TAS_EOT")
display(mods_eot, title="EOT")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.24 | 959.67 | |||
| m_metamint | > 1000 | 1.25 | 0.106 | 0.23 | 913.25 |
| m_minimint | 0.017 | 3.93 | < .001 | 0.17 | 967.79 |
| m_micromint | < 0.001 | 5.28 | < .001 | 0.12 | 1011.59 |
| m_maia | < 0.001 | 2.69 | 0.004 | 0.15 | 1017.28 |
| m_imaia | < 0.001 | 6.57 | < .001 | 0.01 | 1096.63 |
| m_ias | < 0.001 | 6.33 | < .001 | 0.04 | 1061.30 |
| m_bpq | < 0.001 | 6.99 | < .001 | 0.01 | 1080.86 |
Each model is compared to m_mint.
make_cor(select(df, starts_with("TAS_")),
cbind(mint, metamint, select(df, starts_with("MAIA"), IAS, BPQ)))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

preds_ls <- compare_predictors("LifeSatisfaction")
display(preds_ls$table, title="Life Satisfaction")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.05 | 3104.38 | -0.51 | |||
| MAIA_Trusting | > 1000 | -6.57 | < .001 | 0.28 | 2896.13 | 0.80 |
| BPQ | < 0.001 | 3.09 | 0.001 | 0.00 | 3141.29 | -0.06 |
| IAS | 0.970 | 0.00 | 0.499 | 0.05 | 3104.44 | 0.04 |
Each model is compared to MINT_Deficit.
preds_ls$p
mods_ls <- compare_models_lm("LifeSatisfaction")
display(mods_ls, title="Life Satisfaction")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.11 | 3123.86 | |||
| m_metamint | > 1000 | 1.89 | 0.030 | 0.09 | 3086.66 |
| m_minimint | > 1000 | 1.82 | 0.035 | 0.09 | 3086.07 |
| m_micromint | > 1000 | 2.88 | 0.002 | 0.06 | 3108.06 |
| m_maia | > 1000 | -6.34 | < .001 | 0.30 | 2922.14 |
| m_imaia | 0.149 | 3.11 | < .001 | 0.04 | 3127.66 |
| m_ias | > 1000 | 2.54 | 0.006 | 0.05 | 3104.44 |
| m_bpq | < 0.001 | 4.51 | < .001 | 0.00 | 3141.29 |
Each model is compared to m_mint.
preds_anx <- compare_predictors("PHQ4_Anxiety")
display(preds_anx$table, title="PHQ4 Anxiety")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_CaCo | 0.20 | 2776.91 | 0.52 | |||
| MAIA_Trusting | 75.10 | -0.29 | 0.385 | 0.21 | 2768.28 | -0.60 |
| BPQ | < 0.001 | 5.58 | < .001 | 0.03 | 2918.47 | 0.33 |
| IAS | < 0.001 | 5.81 | < .001 | 0.02 | 2922.86 | -0.03 |
Each model is compared to MINT_CaCo.
preds_anx$p
mods_anx <- compare_models_lm("PHQ4_Anxiety")
display(mods_anx, title="PHQ4 Anxiety")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.27 | 2771.46 | |||
| m_metamint | > 1000 | 2.89 | 0.002 | 0.24 | 2750.78 |
| m_minimint | 44.43 | 3.42 | < .001 | 0.23 | 2763.87 |
| m_micromint | 0.002 | 4.32 | < .001 | 0.20 | 2784.46 |
| m_maia | > 1000 | -2.74 | 0.003 | 0.36 | 2662.72 |
| m_imaia | < 0.001 | 8.10 | < .001 | 0.00 | 2950.21 |
| m_ias | < 0.001 | 7.89 | < .001 | 0.02 | 2922.86 |
| m_bpq | < 0.001 | 7.63 | < .001 | 0.03 | 2918.47 |
Each model is compared to m_mint.
preds_dep <- compare_predictors("PHQ4_Depression")
display(preds_dep$table, title="PHQ4 Depression")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.14 | 2631.44 | 0.65 | |||
| MAIA_Trusting | > 1000 | -2.66 | 0.004 | 0.24 | 2544.32 | -0.55 |
| BPQ | < 0.001 | 5.09 | < .001 | 0.02 | 2728.02 | 0.24 |
| IAS | < 0.001 | 4.15 | < .001 | 0.03 | 2721.19 | -0.02 |
Each model is compared to MINT_Deficit.
preds_dep$p
mods_dep <- compare_models_lm("PHQ4_Depression")
display(mods_dep, title="PHQ4 Depression")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.19 | 2649.42 | |||
| m_metamint | > 1000 | 2.41 | 0.008 | 0.17 | 2618.44 |
| m_minimint | > 1000 | 2.81 | 0.002 | 0.15 | 2631.95 |
| m_micromint | 1.31 | 3.73 | < .001 | 0.14 | 2648.89 |
| m_maia | > 1000 | -3.43 | < .001 | 0.30 | 2523.07 |
| m_imaia | < 0.001 | 6.78 | < .001 | 0.01 | 2750.60 |
| m_ias | < 0.001 | 6.12 | < .001 | 0.03 | 2721.19 |
| m_bpq | < 0.001 | 6.17 | < .001 | 0.02 | 2728.02 |
Each model is compared to m_mint.
make_cor(select(df, LifeSatisfaction, starts_with("PHQ4_")),
cbind(mint, metamint, select(df, starts_with("MAIA"), IAS, BPQ)))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

preds_arou <- compare_predictors("ERS_Arousal")
display(preds_arou$table, title="ERS Arousal")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.20 | 2158.88 | 0.58 | |||
| MAIA_NotWorrying | 0.018 | 0.24 | 0.403 | 0.19 | 2166.87 | -0.43 |
| BPQ | < 0.001 | 5.40 | < .001 | 0.04 | 2293.10 | 0.24 |
| IAS | < 0.001 | 6.18 | < .001 | 0.00 | 2317.10 | -0.01 |
Each model is compared to MINT_Deficit.
preds_arou$p
mods_arou <- compare_models_lm("ERS_Arousal")
display(mods_arou, title="ERS Arousal")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.23 | 2193.71 | |||
| m_metamint | > 1000 | 2.26 | 0.012 | 0.21 | 2162.32 |
| m_minimint | 0.742 | 3.66 | < .001 | 0.17 | 2194.31 |
| m_micromint | < 0.001 | 4.49 | < .001 | 0.14 | 2220.35 |
| m_maia | > 1000 | -2.72 | 0.003 | 0.32 | 2084.82 |
| m_imaia | < 0.001 | 5.65 | < .001 | 0.04 | 2302.61 |
| m_ias | < 0.001 | 7.08 | < .001 | 0.00 | 2317.10 |
| m_bpq | < 0.001 | 6.41 | < .001 | 0.04 | 2293.10 |
Each model is compared to m_mint.
preds_sens <- compare_predictors("ERS_Sensitivity")
display(preds_sens$table, title="ERS Sensitivity")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.18 | 2322.13 | 0.60 | |||
| MAIA_NotWorrying | > 1000 | -1.16 | 0.122 | 0.22 | 2284.12 | -0.52 |
| BPQ | < 0.001 | 5.32 | < .001 | 0.03 | 2442.94 | 0.24 |
| IAS | < 0.001 | 6.03 | < .001 | 0.00 | 2462.74 | -0.01 |
Each model is compared to MINT_Deficit.
preds_sens$p
mods_sens <- compare_models_lm("ERS_Sensitivity")
display(mods_sens, title="ERS Sensitivity")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.21 | 2354.48 | |||
| m_metamint | > 1000 | 2.58 | 0.005 | 0.18 | 2327.69 |
| m_minimint | > 1000 | 2.96 | 0.002 | 0.17 | 2336.80 |
| m_micromint | 0.010 | 4.12 | < .001 | 0.14 | 2363.76 |
| m_maia | > 1000 | -3.02 | 0.001 | 0.30 | 2242.73 |
| m_imaia | < 0.001 | 6.02 | < .001 | 0.02 | 2461.56 |
| m_ias | < 0.001 | 6.85 | < .001 | 0.00 | 2462.74 |
| m_bpq | < 0.001 | 6.21 | < .001 | 0.03 | 2442.94 |
Each model is compared to m_mint.
preds_pers <- compare_predictors("ERS_Persistence")
display(preds_pers$table, title="ERS Persistence")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.18 | 2230.34 | 0.58 | |||
| MAIA_NotWorrying | > 1000 | -0.49 | 0.314 | 0.20 | 2214.67 | -0.46 |
| BPQ | < 0.001 | 5.50 | < .001 | 0.02 | 2360.53 | 0.20 |
| IAS | < 0.001 | 5.16 | < .001 | 0.02 | 2360.32 | -0.02 |
Each model is compared to MINT_Deficit.
preds_pers$p
mods_pers <- compare_models_lm("ERS_Persistence")
display(mods_pers, title="ERS Persistence")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.21 | 2268.66 | |||
| m_metamint | > 1000 | 2.10 | 0.018 | 0.19 | 2233.85 |
| m_minimint | 392.09 | 3.11 | < .001 | 0.17 | 2256.71 |
| m_micromint | 0.115 | 3.72 | < .001 | 0.15 | 2272.99 |
| m_maia | > 1000 | -2.71 | 0.003 | 0.30 | 2162.52 |
| m_imaia | < 0.001 | 5.70 | < .001 | 0.02 | 2374.26 |
| m_ias | < 0.001 | 6.18 | < .001 | 0.02 | 2360.32 |
| m_bpq | < 0.001 | 6.20 | < .001 | 0.02 | 2360.53 |
Each model is compared to m_mint.
make_cor(select(df, starts_with("ERS_")),
cbind(mint, metamint, select(df, starts_with("MAIA"), IAS, BPQ)))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

make_cor(select(df, starts_with("MINT"), -matches("[[:digit:]]")),
select(df, starts_with("CEFSA")))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

make_cor(select(df, starts_with("MAIA"), IAS, BPQ),
select(df, starts_with("CEFSA")))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

preds_cefsabody <- compare_predictors("CEFSA_Body")
display(preds_cefsabody$table, title="CEFSA - Body")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_CaCo | 0.23 | 1796.97 | 0.29 | |||
| MAIA_Trusting | < 0.001 | 1.33 | 0.092 | 0.18 | 1842.88 | -0.29 |
| BPQ | < 0.001 | 5.60 | < .001 | 0.03 | 1963.32 | 0.18 |
| IAS | < 0.001 | 4.41 | < .001 | 0.06 | 1941.95 | -0.02 |
Each model is compared to MINT_CaCo.
preds_cefsabody$p
mods_cefsabody <- compare_models_lm("CEFSA_Body")
display(mods_cefsabody, title="CEFSA - Body")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.31 | 1775.41 | |||
| m_metamint | > 1000 | 2.24 | 0.013 | 0.29 | 1746.37 |
| m_minimint | 4.55 | 3.36 | < .001 | 0.27 | 1772.38 |
| m_micromint | < 0.001 | 4.20 | < .001 | 0.24 | 1799.86 |
| m_maia | < 0.001 | 3.04 | 0.001 | 0.22 | 1849.23 |
| m_imaia | < 0.001 | 8.40 | < .001 | 0.00 | 1999.73 |
| m_ias | < 0.001 | 7.18 | < .001 | 0.06 | 1941.95 |
| m_bpq | < 0.001 | 7.81 | < .001 | 0.03 | 1963.32 |
Each model is compared to m_mint.
preds_cefsaself <- compare_predictors("CEFSA_Self")
display(preds_cefsaself$table, title="CEFSA - Self")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.21 | 2032.82 | 0.56 | |||
| MAIA_Trusting | < 0.001 | 2.75 | 0.003 | 0.12 | 2110.11 | -0.28 |
| BPQ | < 0.001 | 6.87 | < .001 | 0.02 | 2191.47 | 0.17 |
| IAS | < 0.001 | 3.87 | < .001 | 0.07 | 2149.73 | -0.03 |
Each model is compared to MINT_Deficit.
preds_cefsaself$p
mods_cefsaself <- compare_models_lm("CEFSA_Self")
display(mods_cefsaself, title="CEFSA - Self")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.28 | 2034.87 | |||
| m_metamint | > 1000 | 2.15 | 0.016 | 0.26 | 2003.23 |
| m_minimint | 6.92 | 3.46 | < .001 | 0.23 | 2031.00 |
| m_micromint | < 0.001 | 4.65 | < .001 | 0.19 | 2069.05 |
| m_maia | < 0.001 | 3.21 | < .001 | 0.18 | 2106.09 |
| m_imaia | < 0.001 | 8.22 | < .001 | 0.00 | 2218.46 |
| m_ias | < 0.001 | 6.08 | < .001 | 0.07 | 2149.73 |
| m_bpq | < 0.001 | 7.76 | < .001 | 0.02 | 2191.47 |
Each model is compared to m_mint.
preds_cefsaemo <- compare_predictors("CEFSA_Emotion")
display(preds_cefsaemo$table, title="CEFSA - Emotion")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.14 | 2143.82 | 0.47 | |||
| MAIA_Trusting | > 1000 | -0.88 | 0.189 | 0.17 | 2118.54 | -0.34 |
| BPQ | < 0.001 | 5.23 | < .001 | 0.01 | 2250.82 | 0.09 |
| IAS | 0.010 | 0.32 | 0.376 | 0.13 | 2153.04 | -0.04 |
Each model is compared to MINT_Deficit.
preds_cefsaemo$p
mods_cefsaemo <- compare_models_lm("CEFSA_Emotion")
display(mods_cefsaemo, title="CEFSA - Emotion")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.27 | 2089.99 | |||
| m_metamint | > 1000 | 2.17 | 0.015 | 0.25 | 2060.65 |
| m_minimint | < 0.001 | 4.33 | < .001 | 0.19 | 2112.18 |
| m_micromint | < 0.001 | 5.21 | < .001 | 0.15 | 2150.24 |
| m_maia | < 0.001 | 2.39 | 0.008 | 0.19 | 2144.24 |
| m_imaia | < 0.001 | 6.29 | < .001 | 0.05 | 2228.72 |
| m_ias | < 0.001 | 4.38 | < .001 | 0.13 | 2153.04 |
| m_bpq | < 0.001 | 7.78 | < .001 | 0.01 | 2250.82 |
Each model is compared to m_mint.
preds_cefsareal <- compare_predictors("CEFSA_Reality")
display(preds_cefsareal$table, title="CEFSA - Reality")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.18 | 1899.23 | 0.46 | |||
| MAIA_Trusting | < 0.001 | 1.14 | 0.127 | 0.14 | 1930.10 | -0.27 |
| BPQ | < 0.001 | 6.63 | < .001 | 0.01 | 2034.63 | 0.13 |
| IAS | < 0.001 | 3.97 | < .001 | 0.06 | 1999.65 | -0.02 |
Each model is compared to MINT_Deficit.
preds_cefsareal$p
mods_cefsareal <- compare_models_lm("CEFSA_Reality")
display(mods_cefsareal, title="CEFSA - Reality")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.24 | 1906.86 | |||
| m_metamint | > 1000 | 1.69 | 0.046 | 0.23 | 1866.90 |
| m_minimint | 189.67 | 3.06 | 0.001 | 0.20 | 1896.37 |
| m_micromint | < 0.001 | 4.24 | < .001 | 0.16 | 1931.22 |
| m_maia | < 0.001 | 2.30 | 0.011 | 0.18 | 1946.66 |
| m_imaia | < 0.001 | 7.36 | < .001 | 0.01 | 2053.67 |
| m_ias | < 0.001 | 5.98 | < .001 | 0.06 | 1999.65 |
| m_bpq | < 0.001 | 7.22 | < .001 | 0.01 | 2034.63 |
Each model is compared to m_mint.
preds_cefsacon <- compare_predictors("CEFSA_Connection")
display(preds_cefsacon$table, title="CEFSA - Connection")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.17 | 2273.53 | 0.58 | |||
| MAIA_Trusting | > 1000 | -2.08 | 0.019 | 0.25 | 2201.91 | -0.46 |
| BPQ | < 0.001 | 5.89 | < .001 | 0.01 | 2402.97 | 0.16 |
| IAS | < 0.001 | 2.58 | 0.005 | 0.09 | 2346.87 | -0.03 |
Each model is compared to MINT_Deficit.
preds_cefsacon$p
mods_cefsacon <- compare_models_lm("CEFSA_Connection")
display(mods_cefsacon, title="CEFSA - Connection")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.25 | 2263.13 | |||
| m_metamint | > 1000 | 2.38 | 0.009 | 0.23 | 2235.43 |
| m_minimint | 21.99 | 3.43 | < .001 | 0.21 | 2256.94 |
| m_micromint | < 0.001 | 4.52 | < .001 | 0.17 | 2292.99 |
| m_maia | > 1000 | -0.73 | 0.233 | 0.28 | 2220.93 |
| m_imaia | < 0.001 | 7.32 | < .001 | 0.02 | 2409.99 |
| m_ias | < 0.001 | 5.31 | < .001 | 0.09 | 2346.87 |
| m_bpq | < 0.001 | 7.55 | < .001 | 0.01 | 2402.97 |
Each model is compared to m_mint.
preds_cefsagency <- compare_predictors("CEFSA_Agency")
display(preds_cefsagency$table, title="CEFSA - Agency")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.18 | 2122.91 | 0.54 | |||
| MAIA_Trusting | < 0.001 | 1.07 | 0.142 | 0.15 | 2153.22 | -0.32 |
| BPQ | < 0.001 | 6.30 | < .001 | 0.02 | 2260.65 | 0.15 |
| IAS | < 0.001 | 3.97 | < .001 | 0.06 | 2227.64 | -0.03 |
Each model is compared to MINT_Deficit.
preds_cefsagency$p
mods_cefsagency <- compare_models_lm("CEFSA_Agency")
display(mods_cefsagency, title="CEFSA - Agency")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.25 | 2124.18 | |||
| m_metamint | > 1000 | 2.21 | 0.014 | 0.23 | 2092.38 |
| m_minimint | < 0.001 | 4.14 | < .001 | 0.18 | 2141.94 |
| m_micromint | < 0.001 | 4.80 | < .001 | 0.15 | 2163.94 |
| m_maia | < 0.001 | 2.14 | 0.016 | 0.19 | 2164.45 |
| m_imaia | < 0.001 | 7.77 | < .001 | 0.01 | 2279.55 |
| m_ias | < 0.001 | 6.23 | < .001 | 0.06 | 2227.64 |
| m_bpq | < 0.001 | 7.44 | < .001 | 0.02 | 2260.65 |
Each model is compared to m_mint.
make_cor(select(df, starts_with("MINT"), -matches("[[:digit:]]")),
select(df, starts_with("CERQ")))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

make_cor(select(df, starts_with("MAIA"), IAS, BPQ),
select(df, starts_with("CERQ")))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

preds_cerqreapp <- compare_predictors("CERQ_PositiveReappraisal")
display(preds_cerqreapp$table, title="CERQ - Positive Reappraisal")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Awareness | 0.07 | 1985.50 | 0.31 | |||
| MAIA_Trusting | > 1000 | -3.13 | < .001 | 0.15 | 1912.14 | 0.28 |
| BPQ | < 0.001 | 2.12 | 0.017 | 0.02 | 2020.91 | 0.15 |
| IAS | 0.332 | 0.17 | 0.431 | 0.06 | 1987.71 | 0.02 |
Each model is compared to MINT_Awareness.
preds_cerqreapp$p
mods_cerqreapp <- compare_models_lm("CERQ_PositiveReappraisal")
display(mods_cerqreapp, title="CERQ - Positive Reappraisal")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.10 | 2024.82 | |||
| m_metamint | > 1000 | 2.23 | 0.013 | 0.07 | 1994.86 |
| m_minimint | > 1000 | 2.76 | 0.003 | 0.05 | 2007.83 |
| m_micromint | 44.67 | 3.16 | < .001 | 0.04 | 2017.22 |
| m_maia | > 1000 | -3.61 | < .001 | 0.21 | 1908.44 |
| m_imaia | > 1000 | -0.55 | 0.293 | 0.11 | 1960.87 |
| m_ias | > 1000 | 1.67 | 0.048 | 0.06 | 1987.71 |
| m_bpq | 7.06 | 3.30 | < .001 | 0.02 | 2020.91 |
Each model is compared to m_mint.
preds_cerqrefocus <- compare_predictors("CERQ_PositiveRefocusing")
display(preds_cerqrefocus$table, title="CERQ - Positive Refocusing")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_RelA | 0.06 | 2108.34 | 0.21 | |||
| MAIA_SelfRegulation | > 1000 | -2.90 | 0.002 | 0.14 | 2045.49 | 0.29 |
| BPQ | < 0.001 | 2.99 | 0.001 | 0.00 | 2152.33 | 0.05 |
| IAS | < 0.001 | 2.07 | 0.019 | 0.02 | 2135.43 | 0.02 |
Each model is compared to MINT_RelA.
preds_cerqrefocus$p
mods_cerqrefocus <- compare_models_lm("CERQ_PositiveRefocusing")
display(mods_cerqrefocus, title="CERQ - Positive Refocusing")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.07 | 2163.05 | |||
| m_metamint | > 1000 | 2.45 | 0.007 | 0.04 | 2136.82 |
| m_minimint | > 1000 | 2.28 | 0.011 | 0.04 | 2133.84 |
| m_micromint | > 1000 | 2.77 | 0.003 | 0.03 | 2147.23 |
| m_maia | > 1000 | -4.17 | < .001 | 0.19 | 2043.21 |
| m_imaia | > 1000 | -0.94 | 0.172 | 0.10 | 2092.09 |
| m_ias | > 1000 | 2.46 | 0.007 | 0.02 | 2135.43 |
| m_bpq | 213.01 | 3.31 | < .001 | 0.00 | 2152.33 |
Each model is compared to m_mint.
preds_cerqpersp <- compare_predictors("CERQ_Perspective")
display(preds_cerqpersp$table, title="CERQ - Perspective")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Awareness | 0.04 | 1939.81 | 0.24 | |||
| MAIA_AttentionRegulation | > 1000 | -1.81 | 0.035 | 0.08 | 1907.97 | 0.24 |
| BPQ | < 0.001 | 2.23 | 0.013 | 0.01 | 1966.83 | 0.09 |
| IAS | 0.119 | 0.43 | 0.333 | 0.04 | 1944.07 | 0.02 |
Each model is compared to MINT_Awareness.
preds_cerqpersp$p
mods_cerqpersp <- compare_models_lm("CERQ_Perspective")
display(mods_cerqpersp, title="CERQ - Perspective")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.07 | 1988.72 | |||
| m_metamint | > 1000 | 1.68 | 0.046 | 0.05 | 1951.29 |
| m_minimint | > 1000 | 2.05 | 0.020 | 0.04 | 1959.08 |
| m_micromint | > 1000 | 2.42 | 0.008 | 0.03 | 1966.07 |
| m_maia | > 1000 | -3.06 | 0.001 | 0.16 | 1892.96 |
| m_imaia | > 1000 | 1.97 | 0.024 | 0.03 | 1964.20 |
| m_ias | > 1000 | 1.43 | 0.076 | 0.04 | 1944.07 |
| m_bpq | > 1000 | 2.97 | 0.001 | 0.01 | 1966.83 |
Each model is compared to m_mint.
preds_cerqacceptance <- compare_predictors("CERQ_Acceptance")
display(preds_cerqacceptance$table, title="CERQ - Acceptance")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Awareness | 0.02 | 1870.51 | 0.14 | |||
| MAIA_NotDistracting | 5.87 | -0.31 | 0.379 | 0.02 | 1866.98 | -0.13 |
| BPQ | 0.027 | 0.88 | 0.190 | 0.01 | 1877.74 | 0.09 |
| IAS | 0.339 | 0.36 | 0.358 | 0.02 | 1872.68 | 0.01 |
Each model is compared to MINT_Awareness.
preds_cerqacceptance$p
mods_cerqacceptance <- compare_models_lm("CERQ_Acceptance")
display(mods_cerqacceptance, title="CERQ - Acceptance")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.03 | 1923.83 | |||
| m_metamint | > 1000 | 1.39 | 0.082 | 0.02 | 1879.76 |
| m_minimint | > 1000 | 1.68 | 0.047 | 0.02 | 1883.76 |
| m_micromint | > 1000 | 1.92 | 0.027 | 0.01 | 1886.77 |
| m_maia | > 1000 | -0.82 | 0.205 | 0.05 | 1892.94 |
| m_imaia | > 1000 | 0.81 | 0.209 | 0.02 | 1879.39 |
| m_ias | > 1000 | 1.48 | 0.069 | 0.02 | 1872.68 |
| m_bpq | > 1000 | 1.92 | 0.028 | 0.01 | 1877.74 |
Each model is compared to m_mint.
make_cor(select(df, starts_with("MINT"), -matches("[[:digit:]]")),
select(df, starts_with("PI")))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

make_cor(select(df, starts_with("MAIA"), IAS, BPQ),
select(df, starts_with("PI")))Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

preds_pialive <- compare_predictors("PI_Alive")
display(preds_pialive$table, title="Primals - Alive")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Visceroception | 0.07 | 2374.58 | 0.28 | |||
| MAIA_EmotionalAwareness | > 1000 | -2.10 | 0.018 | 0.13 | 2331.48 | 0.39 |
| BPQ | < 0.001 | 3.24 | < .001 | 0.02 | 2418.47 | 0.17 |
| IAS | 0.006 | 0.57 | 0.285 | 0.06 | 2384.87 | 0.03 |
Each model is compared to MINT_Visceroception.
preds_pialive$p
mods_pialive <- compare_models_lm("PI_Alive")
display(mods_pialive, title="Primals - Alive")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.13 | 2394.67 | |||
| m_metamint | > 1000 | 2.65 | 0.004 | 0.09 | 2369.95 |
| m_minimint | > 1000 | 2.32 | 0.010 | 0.10 | 2363.89 |
| m_micromint | 163.03 | 3.22 | < .001 | 0.08 | 2384.48 |
| m_maia | > 1000 | -1.60 | 0.055 | 0.17 | 2337.51 |
| m_imaia | > 1000 | -0.74 | 0.229 | 0.15 | 2325.77 |
| m_ias | 134.32 | 2.99 | 0.001 | 0.06 | 2384.87 |
| m_bpq | < 0.001 | 4.91 | < .001 | 0.02 | 2418.47 |
Each model is compared to m_mint.
preds_pisafe <- compare_predictors("PI_Safe")
display(preds_pisafe$table, title="Primals - Safe")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.12 | 1775.84 | -0.34 | |||
| MAIA_Trusting | > 1000 | -0.96 | 0.168 | 0.15 | 1750.87 | 0.25 |
| BPQ | < 0.001 | 3.15 | < .001 | 0.05 | 1836.76 | -0.20 |
| IAS | < 0.001 | 3.17 | < .001 | 0.04 | 1842.84 | 0.02 |
Each model is compared to MINT_Deficit.
preds_pisafe$p
mods_pisafe <- compare_models_lm("PI_Safe")
display(mods_pisafe, title="Primals - Safe")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.18 | 1794.07 | |||
| m_metamint | > 1000 | 2.32 | 0.010 | 0.15 | 1763.83 |
| m_minimint | 1.91 | 3.62 | < .001 | 0.12 | 1792.78 |
| m_micromint | < 0.001 | 4.52 | < .001 | 0.09 | 1819.00 |
| m_maia | > 1000 | -1.54 | 0.062 | 0.23 | 1728.15 |
| m_imaia | < 0.001 | 6.14 | < .001 | 0.00 | 1883.38 |
| m_ias | < 0.001 | 4.90 | < .001 | 0.04 | 1842.84 |
| m_bpq | < 0.001 | 4.46 | < .001 | 0.05 | 1836.76 |
Each model is compared to m_mint.
preds_pient <- compare_predictors("PI_Enticing")
display(preds_pient$table, title="Primals - Enticing")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Awareness | 0.07 | 1725.24 | 0.26 | |||
| MAIA_Trusting | > 1000 | -2.86 | 0.002 | 0.14 | 1663.95 | 0.22 |
| BPQ | < 0.001 | 3.40 | < .001 | 0.00 | 1776.61 | -0.01 |
| IAS | 0.391 | 0.12 | 0.454 | 0.06 | 1727.12 | 0.02 |
Each model is compared to MINT_Awareness.
preds_pient$p
mods_pient <- compare_models_lm("PI_Enticing")
display(mods_pient, title="Primals - Enticing")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.11 | 1753.01 | |||
| m_metamint | > 1000 | 1.59 | 0.056 | 0.10 | 1710.93 |
| m_minimint | 395.91 | 3.25 | < .001 | 0.06 | 1741.05 |
| m_micromint | 1.92 | 3.51 | < .001 | 0.05 | 1751.70 |
| m_maia | > 1000 | -1.93 | 0.027 | 0.17 | 1685.31 |
| m_imaia | 0.017 | 3.14 | < .001 | 0.04 | 1761.18 |
| m_ias | > 1000 | 2.04 | 0.021 | 0.06 | 1727.12 |
| m_bpq | < 0.001 | 4.76 | < .001 | 0.00 | 1776.61 |
Each model is compared to m_mint.
preds_pigood <- compare_predictors("PI_Good")
display(preds_pigood$table, title="Primals - Good")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.10 | 1526.10 | -0.26 | |||
| MAIA_Trusting | > 1000 | -2.94 | 0.002 | 0.20 | 1444.02 | 0.23 |
| BPQ | < 0.001 | 3.87 | < .001 | 0.02 | 1593.21 | -0.10 |
| IAS | < 0.001 | 1.27 | 0.102 | 0.07 | 1554.19 | 0.02 |
Each model is compared to MINT_Deficit.
preds_pigood$p
mods_pigood <- compare_models_lm("PI_Good")
display(mods_pigood, title="Primals - Good")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.17 | 1530.74 | |||
| m_metamint | > 1000 | 1.77 | 0.038 | 0.16 | 1490.62 |
| m_minimint | 2.92 | 3.69 | < .001 | 0.12 | 1528.60 |
| m_micromint | < 0.001 | 4.29 | < .001 | 0.09 | 1553.14 |
| m_maia | > 1000 | -2.28 | 0.011 | 0.25 | 1438.30 |
| m_imaia | < 0.001 | 5.54 | < .001 | 0.02 | 1608.05 |
| m_ias | < 0.001 | 3.65 | < .001 | 0.07 | 1554.19 |
| m_bpq | < 0.001 | 5.22 | < .001 | 0.02 | 1593.21 |
Each model is compared to m_mint.
compare_models_glm <- function(outcome="Disorders_Psychiatric_Mood", title="", subtitle = "") {
# Full Questionnaires
m_mint <- glm(as.formula(paste0(outcome, " ~ .")),
data = cbind(mint, df[outcome]),
family = "binomial")
m_metamint <- glm(as.formula(paste0(outcome, " ~ .")),
data = cbind(metamint, df[outcome]),
family = "binomial")
m_minimint <- glm(as.formula(paste0(outcome, " ~ .")),
data = cbind(minimint, df[outcome]),
family = "binomial")
m_micromint <- glm(as.formula(paste0(outcome, " ~ .")),
data = cbind(micromint, df[outcome]),
family = "binomial")
m_ias <- glm(as.formula(paste0(outcome, " ~ IAS")),
data = df,
family = "binomial")
m_maia <- glm(as.formula(paste0(outcome, " ~ .")),
data = select(df, all_of(outcome), starts_with("MAIA")),
family = "binomial")
m_imaia <- glm(as.formula(paste0(outcome, " ~ .")),
data = select(df, all_of(outcome),
starts_with("MAIA"),
-contains("NotWorrying"),
-contains("NotDistracting")),
family = "binomial")
m_imaia <- glm(as.formula(paste0(outcome, " ~ .")),
data = select(df, all_of(outcome),
# MAIA_AttentionRegulation, # remove?
# MAIA_SelfRegulation, # remove?
# MAIA_Trusting, # remove?
MAIA_BodyListening,
MAIA_EmotionalAwareness,
MAIA_Noticing),
family = "binomial")
m_bpq <- glm(as.formula(paste0(outcome, " ~ BPQ")),
data = df,
family = "binomial")
models_full <- list(m_mint=m_mint, m_metamint=m_metamint,
m_minimint=m_minimint, m_micromint=m_micromint,
m_maia=m_maia, m_imaia=m_imaia,
m_ias=m_ias, m_bpq=m_bpq)
table <- table_models(models_full)
get_auc <- function(m) {
roc <- as.data.frame(performance_roc(m))
auc <- bayestestR::area_under_curve(roc$Specificity, roc$Sensitivity)
paste0("AUC = ", insight::format_percent(auc))
}
mods_order <- c("Mint", "metaMint", "miniMint", "microMint", "MAIA", "iMAIA", "IAS", "BPQ")
p <- rbind(
mutate(as.data.frame(performance_roc(m_mint)), Predictor = "Mint"),
mutate(as.data.frame(performance_roc(m_metamint)), Predictor = "metaMint"),
mutate(as.data.frame(performance_roc(m_minimint)), Predictor = "miniMint"),
mutate(as.data.frame(performance_roc(m_micromint)), Predictor = "microMint"),
mutate(as.data.frame(performance_roc(m_maia)), Predictor = "MAIA"),
mutate(as.data.frame(performance_roc(m_imaia)), Predictor = "iMAIA"),
mutate(as.data.frame(performance_roc(m_ias)), Predictor = "IAS"),
mutate(as.data.frame(performance_roc(m_bpq)), Predictor = "BPQ")
) |>
mutate(Predictor = fct_relevel(Predictor, mods_order)) |>
ggplot(aes(x=Specificity)) +
geom_abline(intercept=0, slope=1, color="gray", linewidth=1) +
geom_line(aes(y=Sensitivity, color=Predictor), linewidth=1.5, alpha=0.9) +
geom_label(data=data.frame(
label = c(get_auc(m_mint), get_auc(m_metamint),
get_auc(m_minimint), get_auc(m_micromint),
get_auc(m_maia), get_auc(m_imaia), get_auc(m_ias), get_auc(m_bpq)),
Predictor = mods_order,
x = 0.8,
y = c(0.55, 0.475, 0.425, 0.375, 0.30, 0.25, 0.20, 0.15)),
aes(x=x, y=y, label=label), color=colors[mods_order], size=3) +
labs(x= "1 - Specificity (False Positive Rate)", y="Sensitivity (True Positive Rate)",
color="Questionnaire", title = title, subtitle=subtitle) +
scale_color_manual(values=colors) +
scale_x_continuous(labels=scales::percent) +
scale_y_continuous(labels=scales::percent) +
theme_minimal() +
theme(plot.title = element_text(face="bold"))
list(table=table, p=p)
}preds_mood <- compare_predictors("Disorders_Psychiatric_Mood")
display(preds_mood$table, title="Mood Disorders")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.07 | 647.74 | 0.75 | |||
| MAIA_Trusting | > 1000 | -1.10 | 0.136 | 0.10 | 628.01 | -0.84 |
| BPQ | < 0.001 | 3.41 | < .001 | 0.01 | 693.45 | 0.25 |
| IAS | < 0.001 | 3.45 | < .001 | 0.00 | 697.06 | -0.16 |
Each model is compared to MINT_Deficit.
preds_mood$p
mods_mood <- compare_models_glm("Disorders_Psychiatric_Mood")
display(mods_mood$table, title="Mood Disorders")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.11 | 693.59 | |||
| m_metamint | > 1000 | 1.57 | 0.058 | 0.09 | 651.91 |
| m_minimint | > 1000 | 2.29 | 0.011 | 0.08 | 660.53 |
| m_micromint | > 1000 | 2.29 | 0.011 | 0.07 | 664.76 |
| m_maia | > 1000 | -2.67 | 0.004 | 0.18 | 624.14 |
| m_imaia | < 0.001 | 4.13 | < .001 | 0.00 | 710.98 |
| m_ias | 0.176 | 4.09 | < .001 | 0.00 | 697.06 |
| m_bpq | 1.07 | 4.11 | < .001 | 0.01 | 693.45 |
Each model is compared to m_mint.
mods_mood$p
preds_moodt <- compare_predictors("Disorders_Psychiatric_MoodTreatment")
display(preds_moodt$table, title="Mood Disorders (with Treatment)")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.06 | 452.33 | 0.84 | |||
| MAIA_Trusting | 387.73 | -0.81 | 0.210 | 0.08 | 440.41 | -0.90 |
| BPQ | < 0.001 | 3.07 | 0.001 | 0.00 | 489.91 | 0.24 |
| IAS | < 0.001 | 2.76 | 0.003 | 0.01 | 488.61 | -0.27 |
Each model is compared to MINT_Deficit.
preds_moodt$p
mods_moodt <- compare_models_glm("Disorders_Psychiatric_MoodTreatment",
title="Mood Disorders", subtitle = "MDD, GAD, Bipolar (with Treatment)")
display(mods_moodt$table, title="Mood Disorders (with Treatment)")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.09 | 501.95 | |||
| m_metamint | > 1000 | 1.46 | 0.072 | 0.08 | 456.71 |
| m_minimint | > 1000 | 2.58 | 0.005 | 0.06 | 469.91 |
| m_micromint | > 1000 | 2.21 | 0.014 | 0.06 | 469.83 |
| m_maia | > 1000 | -2.29 | 0.011 | 0.17 | 445.54 |
| m_imaia | 0.243 | 3.61 | < .001 | 0.00 | 504.78 |
| m_ias | 790.65 | 3.59 | < .001 | 0.01 | 488.61 |
| m_bpq | 412.07 | 3.65 | < .001 | 0.00 | 489.91 |
Each model is compared to m_mint.
mods_moodt$p
preds_anxdis <- compare_predictors("Disorders_Psychiatric_Anxiety")
display(preds_anxdis$table, title="Anxiety Disorders")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_CaCo | 0.05 | 686.43 | 0.58 | |||
| MAIA_Trusting | > 1000 | -1.68 | 0.047 | 0.09 | 658.48 | -0.78 |
| BPQ | < 0.001 | 2.63 | 0.004 | 0.01 | 716.72 | 0.26 |
| IAS | < 0.001 | 3.05 | 0.001 | 0.00 | 722.92 | -0.10 |
Each model is compared to MINT_CaCo.
preds_anxdis$p
mods_anxdis <- compare_models_glm("Disorders_Psychiatric_Anxiety")
display(mods_anxdis$table, title="Anxiety Disorders")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.09 | 729.31 | |||
| m_metamint | > 1000 | 1.87 | 0.030 | 0.06 | 693.26 |
| m_minimint | > 1000 | 1.98 | 0.024 | 0.06 | 692.87 |
| m_micromint | > 1000 | 2.22 | 0.013 | 0.05 | 699.61 |
| m_maia | > 1000 | -3.45 | < .001 | 0.19 | 646.25 |
| m_imaia | 0.068 | 3.74 | < .001 | 0.00 | 734.68 |
| m_ias | 24.33 | 3.86 | < .001 | 0.00 | 722.92 |
| m_bpq | 540.19 | 3.64 | < .001 | 0.01 | 716.72 |
Each model is compared to m_mint.
mods_anxdis$p
preds_anxdist <- compare_predictors("Disorders_Psychiatric_AnxietyTreatment")
display(preds_anxdist$table, title="Anxiety Disorders (with Treatment)")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.03 | 398.02 | 0.58 | |||
| MAIA_Trusting | 237.81 | -0.98 | 0.163 | 0.05 | 387.08 | -0.71 |
| BPQ | 0.002 | 1.49 | 0.068 | 0.01 | 410.01 | 0.30 |
| IAS | < 0.001 | 1.78 | 0.038 | 0.00 | 413.57 | -0.13 |
Each model is compared to MINT_Deficit.
preds_anxdist$p
mods_anxdist <- compare_models_glm("Disorders_Psychiatric_AnxietyTreatment")
display(mods_anxdist$table, title="Anxiety Disorders (with Treatment)")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.04 | 457.93 | |||
| m_metamint | > 1000 | 0.85 | 0.197 | 0.03 | 409.34 |
| m_minimint | > 1000 | 1.32 | 0.094 | 0.02 | 412.24 |
| m_micromint | > 1000 | 1.43 | 0.076 | 0.02 | 415.54 |
| m_maia | > 1000 | -2.62 | 0.004 | 0.13 | 400.96 |
| m_imaia | > 1000 | 1.71 | 0.043 | 0.01 | 422.88 |
| m_ias | > 1000 | 1.91 | 0.028 | 0.00 | 413.57 |
| m_bpq | > 1000 | 1.79 | 0.037 | 0.01 | 410.01 |
Each model is compared to m_mint.
mods_anxdist$p
preds_eat <- compare_predictors("Disorders_Psychiatric_Eating")
display(preds_eat$table, title="Eating Disorders")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_CaCo | 0.03 | 166.00 | 0.96 | |||
| MAIA_Trusting | 480.64 | -1.21 | 0.113 | 0.06 | 153.65 | -1.29 |
| BPQ | < 0.001 | 1.96 | 0.025 | 0.00 | 182.40 | 0.00 |
| IAS | 0.006 | 1.06 | 0.145 | 0.01 | 176.17 | -0.58 |
Each model is compared to MINT_CaCo.
preds_eat$p
mods_eat <- compare_models_glm("Disorders_Psychiatric_Eating")
display(mods_eat$table, title="Eating Disorders")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.07 | 217.25 | |||
| m_metamint | > 1000 | 0.63 | 0.265 | 0.06 | 167.68 |
| m_minimint | > 1000 | 1.26 | 0.104 | 0.06 | 169.36 |
| m_micromint | > 1000 | 1.83 | 0.034 | 0.04 | 173.90 |
| m_maia | > 1000 | -0.41 | 0.340 | 0.10 | 192.60 |
| m_imaia | > 1000 | 2.42 | 0.008 | 0.00 | 193.17 |
| m_ias | > 1000 | 2.25 | 0.012 | 0.01 | 176.17 |
| m_bpq | > 1000 | 2.57 | 0.005 | 0.00 | 182.40 |
Each model is compared to m_mint.
mods_eat$p
preds_addict <- compare_predictors("Disorders_Psychiatric_Addiction")
display(preds_addict$table, title="Addiction")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_SexS | 0.01 | 207.84 | 0.45 | |||
| MAIA_Noticing | 0.575 | 0.30 | 0.381 | 0.00 | 208.95 | 0.32 |
| BPQ | 0.379 | 0.41 | 0.340 | 0.00 | 209.78 | 0.25 |
| IAS | 0.244 | 0.70 | 0.242 | 0.00 | 210.66 | 0.14 |
Each model is compared to MINT_SexS.
preds_addict$p
mods_addict <- compare_models_glm("Disorders_Psychiatric_Addiction")
display(mods_addict$table, title="Addiction")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.01 | 269.86 | |||
| m_metamint | > 1000 | 0.82 | 0.206 | 0.00 | 220.52 |
| m_minimint | > 1000 | 0.94 | 0.174 | 0.00 | 222.45 |
| m_micromint | > 1000 | 0.93 | 0.176 | 0.00 | 221.82 |
| m_maia | > 1000 | -0.22 | 0.414 | 0.01 | 248.58 |
| m_imaia | > 1000 | 0.87 | 0.191 | 0.00 | 221.76 |
| m_ias | > 1000 | 1.23 | 0.109 | 0.00 | 210.66 |
| m_bpq | > 1000 | 1.01 | 0.157 | 0.00 | 209.78 |
Each model is compared to m_mint.
mods_addict$p
preds_adhd <- compare_predictors("Disorders_Psychiatric_ADHD")
display(preds_adhd$table, title="ADHD")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.03 | 432.89 | 0.60 | |||
| MAIA_Trusting | 0.007 | 1.12 | 0.132 | 0.01 | 442.91 | -0.40 |
| BPQ | < 0.001 | 2.13 | 0.017 | 0.00 | 452.19 | 0.11 |
| IAS | 0.040 | 0.55 | 0.290 | 0.02 | 439.30 | -0.48 |
Each model is compared to MINT_Deficit.
preds_adhd$p
mods_adhd <- compare_models_glm("Disorders_Psychiatric_ADHD",
title = "ADHD", subtitle = "Attention Deficit/Hyperactivity Disorder")
display(mods_adhd$table, title="ADHD")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.07 | 478.24 | |||
| m_metamint | > 1000 | 1.58 | 0.057 | 0.05 | 434.60 |
| m_minimint | > 1000 | 1.62 | 0.053 | 0.05 | 438.84 |
| m_micromint | > 1000 | 2.24 | 0.012 | 0.02 | 450.87 |
| m_maia | 0.244 | 1.80 | 0.036 | 0.03 | 481.06 |
| m_imaia | 905.92 | 2.66 | 0.004 | 0.00 | 464.63 |
| m_ias | > 1000 | 2.14 | 0.016 | 0.02 | 439.30 |
| m_bpq | > 1000 | 2.75 | 0.003 | 0.00 | 452.19 |
Each model is compared to m_mint.
mods_adhd$p
preds_asd <- compare_predictors("Disorders_Psychiatric_Autism")
display(preds_asd$table, title="Autism")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.03 | 367.12 | 0.71 | |||
| MAIA_Trusting | 0.076 | 0.58 | 0.280 | 0.02 | 372.28 | -0.58 |
| BPQ | < 0.001 | 2.36 | 0.009 | 0.00 | 388.27 | 0.14 |
| IAS | 0.005 | 0.96 | 0.170 | 0.02 | 377.64 | -0.48 |
Each model is compared to MINT_Deficit.
preds_asd$p
mods_asd <- compare_models_glm("Disorders_Psychiatric_Autism",
title = "ASD", subtitle = "Autism Spectrum Disorder")
display(mods_asd$table, title="Autism")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.08 | 410.51 | |||
| m_metamint | > 1000 | 1.39 | 0.082 | 0.06 | 367.41 |
| m_minimint | > 1000 | 1.89 | 0.029 | 0.04 | 376.40 |
| m_micromint | > 1000 | 2.51 | 0.006 | 0.02 | 389.04 |
| m_maia | 0.168 | 1.76 | 0.039 | 0.03 | 414.07 |
| m_imaia | 139.07 | 2.96 | 0.002 | 0.00 | 400.64 |
| m_ias | > 1000 | 2.35 | 0.009 | 0.02 | 377.64 |
| m_bpq | > 1000 | 2.97 | 0.002 | 0.00 | 388.27 |
Each model is compared to m_mint.
mods_asd$p
preds_bpd <- compare_predictors("Disorders_Psychiatric_Borderline")
display(preds_bpd$table, title="Borderline Personality Disorder")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Derm | 0.01 | 74.62 | 1.59 | |||
| MAIA_EmotionalAwareness | 6.78 | -0.67 | 0.251 | 0.02 | 70.79 | 1.93 |
| BPQ | 0.647 | 0.13 | 0.446 | 0.01 | 75.49 | 1.31 |
| IAS | 0.018 | 1.45 | 0.073 | 0.00 | 82.71 | 0.17 |
Each model is compared to MINT_Derm.
preds_bpd$p
mods_bpd <- compare_models_glm("Disorders_Psychiatric_Borderline")
display(mods_bpd$table, title="Borderline Personality Disorder")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.18 | 120.21 | |||
| m_metamint | > 1000 | 1.79 | 0.037 | 0.02 | 85.38 |
| m_minimint | > 1000 | 1.56 | 0.060 | 0.03 | 85.37 |
| m_micromint | > 1000 | 1.37 | 0.085 | 0.04 | 85.38 |
| m_maia | > 1000 | -0.89 | 0.188 | 0.38 | 87.65 |
| m_imaia | > 1000 | 0.97 | 0.166 | 0.07 | 78.69 |
| m_ias | > 1000 | 1.96 | 0.025 | 0.00 | 82.71 |
| m_bpq | > 1000 | 1.62 | 0.053 | 0.01 | 75.49 |
Each model is compared to m_mint.
mods_bpd$p
preds_afferent <- compare_predictors("Disorders_Somatic_AfferentSensitivity")
display(preds_afferent$table, title="Afferent Sensitivity Symptoms")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.04 | 738.45 | 0.50 | |||
| MAIA_Trusting | 0.003 | 1.07 | 0.143 | 0.02 | 750.22 | -0.36 |
| BPQ | < 0.001 | 2.54 | 0.006 | 0.00 | 763.36 | 0.15 |
| IAS | < 0.001 | 2.52 | 0.006 | 0.00 | 764.33 | -0.12 |
Each model is compared to MINT_Deficit.
preds_afferent$p
mods_afferent <- compare_models_glm("Disorders_Somatic_AfferentSensitivity",
title="Afferent Sensitivity Symptoms",
subtitle = "Migraine, neuropathy, muscle tension, dizziness, ...")
display(mods_afferent$table, title="Afferent Sensitivity Symptoms")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.06 | 787.90 | |||
| m_metamint | > 1000 | 1.85 | 0.032 | 0.04 | 749.77 |
| m_minimint | > 1000 | 1.93 | 0.027 | 0.04 | 750.39 |
| m_micromint | > 1000 | 2.49 | 0.006 | 0.03 | 759.68 |
| m_maia | 43.07 | 0.96 | 0.170 | 0.05 | 780.38 |
| m_imaia | 75.75 | 3.50 | < .001 | 0.00 | 779.25 |
| m_ias | > 1000 | 3.44 | < .001 | 0.00 | 764.33 |
| m_bpq | > 1000 | 3.40 | < .001 | 0.00 | 763.36 |
Each model is compared to m_mint.
mods_afferent$p
preds_central <- compare_predictors("Disorders_Somatic_CentralSensitization")
display(preds_central$table, title="Central Sensitization Symptoms")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.03 | 860.88 | 0.38 | |||
| MAIA_Trusting | 0.607 | 0.09 | 0.464 | 0.03 | 861.87 | -0.36 |
| BPQ | < 0.001 | 2.09 | 0.018 | 0.00 | 879.09 | 0.11 |
| IAS | < 0.001 | 2.06 | 0.020 | 0.00 | 880.18 | -0.06 |
Each model is compared to MINT_Deficit.
preds_central$p
mods_central <- compare_models_glm("Disorders_Somatic_CentralSensitization",
title="Central Sensitization Symptoms",
subtitle = "Fibromyalgia, chronic fatigue, back pain, IBS, ...")
display(mods_central$table, title="Central Sensitization Symptoms")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.05 | 908.13 | |||
| m_metamint | > 1000 | 1.88 | 0.030 | 0.03 | 870.25 |
| m_minimint | > 1000 | 2.19 | 0.014 | 0.03 | 875.21 |
| m_micromint | > 1000 | 2.63 | 0.004 | 0.01 | 884.89 |
| m_maia | > 1000 | -0.57 | 0.285 | 0.06 | 880.20 |
| m_imaia | > 1000 | 2.66 | 0.004 | 0.00 | 890.41 |
| m_ias | > 1000 | 3.04 | 0.001 | 0.00 | 880.18 |
| m_bpq | > 1000 | 2.99 | 0.001 | 0.00 | 879.09 |
Each model is compared to m_mint.
mods_central$p
preds_autonomic <- compare_predictors("Disorders_Somatic_AutonomicDysfunction")
display(preds_autonomic$table, title="Autonomic Dysfunction")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Card | 0.03 | 698.65 | 0.46 | |||
| MAIA_Trusting | < 0.001 | 1.75 | 0.040 | 0.01 | 716.70 | -0.20 |
| BPQ | < 0.001 | 2.13 | 0.017 | 0.00 | 717.74 | 0.17 |
| IAS | < 0.001 | 2.30 | 0.011 | 0.00 | 720.99 | -0.02 |
Each model is compared to MINT_Card.
preds_autonomic$p
mods_autonomic <- compare_models_glm("Disorders_Somatic_AutonomicDysfunction",
title="Autonomic Dysfunction",
subtitle = "Hypermobility, chest pain, hypo/hypertension, ....")
display(mods_autonomic$table, title="Autonomic Dysfunction")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.07 | 736.12 | |||
| m_metamint | 494.59 | 3.07 | 0.001 | 0.01 | 723.71 |
| m_minimint | > 1000 | 2.57 | 0.005 | 0.03 | 711.21 |
| m_micromint | > 1000 | 2.99 | 0.001 | 0.02 | 720.48 |
| m_maia | 0.003 | 1.93 | 0.027 | 0.03 | 748.02 |
| m_imaia | 10.85 | 3.29 | < .001 | 0.00 | 731.35 |
| m_ias | > 1000 | 3.49 | < .001 | 0.00 | 720.99 |
| m_bpq | > 1000 | 3.31 | < .001 | 0.00 | 717.74 |
Each model is compared to m_mint.
mods_autonomic$p
preds_immune <- compare_predictors("Disorders_Somatic_ImmuneInflammatory")
display(preds_immune$table, title="Immune-Inflammatory Symptoms")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Derm | 0.07 | 891.57 | 0.60 | |||
| MAIA_Trusting | < 0.001 | 1.86 | 0.032 | 0.03 | 921.09 | -0.36 |
| BPQ | < 0.001 | 2.87 | 0.002 | 0.01 | 931.00 | 0.26 |
| IAS | < 0.001 | 3.38 | < .001 | 0.00 | 940.64 | -0.09 |
Each model is compared to MINT_Derm.
preds_immune$p
mods_immune <- compare_models_glm("Disorders_Somatic_ImmuneInflammatory",
title="Immune-Inflammatory Symptoms",
subtitle = "Allergies, asthma, eczema, autoimmune, ...")
display(mods_immune$table, title="Immune-Inflammatory Symptoms")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.10 | 934.15 | |||
| m_metamint | > 1000 | 3.07 | 0.001 | 0.05 | 919.15 |
| m_minimint | 33.21 | 3.43 | < .001 | 0.04 | 927.14 |
| m_micromint | 4.52 | 3.56 | < .001 | 0.03 | 931.13 |
| m_maia | 0.018 | 1.66 | 0.049 | 0.06 | 942.14 |
| m_imaia | < 0.001 | 4.16 | < .001 | 0.00 | 951.87 |
| m_ias | 0.039 | 4.36 | < .001 | 0.00 | 940.64 |
| m_bpq | 4.84 | 3.82 | < .001 | 0.01 | 931.00 |
Each model is compared to m_mint.
mods_immune$p
preds_age <- compare_predictors("Age")
display(preds_age$table, title="Age")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Deficit | 0.08 | 6033.91 | -4.78 | |||
| MAIA_NotWorrying | < 0.001 | 1.69 | 0.046 | 0.04 | 6061.48 | 2.74 |
| BPQ | < 0.001 | 3.61 | < .001 | 0.01 | 6084.98 | -1.93 |
| IAS | < 0.001 | 1.64 | 0.050 | 0.04 | 6062.47 | 0.29 |
Each model is compared to MINT_Deficit.
preds_age$p
mods_age <- compare_models_lm("Age")
display(mods_age, title="Age")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.17 | 6024.63 | |||
| m_metamint | 29.55 | 3.38 | < .001 | 0.12 | 6017.86 |
| m_minimint | < 0.001 | 4.60 | < .001 | 0.07 | 6051.36 |
| m_micromint | < 0.001 | 4.62 | < .001 | 0.07 | 6050.92 |
| m_maia | < 0.001 | 3.01 | 0.001 | 0.09 | 6074.60 |
| m_imaia | < 0.001 | 6.34 | < .001 | 0.00 | 6106.72 |
| m_ias | < 0.001 | 4.87 | < .001 | 0.04 | 6062.47 |
| m_bpq | < 0.001 | 6.01 | < .001 | 0.01 | 6084.98 |
Each model is compared to m_mint.
df$Sex <- ifelse(df$Gender == "Female", 1, ifelse(df$Gender == "Male", 0, NA))
preds_sex <- compare_predictors("Sex")
display(preds_sex$table, title="Sex")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_CaCo | 0.06 | 1004.87 | 0.08 | |||
| MAIA_NotWorrying | 0.006 | 0.67 | 0.252 | 0.05 | 1015.26 | -0.10 |
| BPQ | < 0.001 | 3.44 | < .001 | 0.00 | 1050.06 | 0.03 |
| IAS | < 0.001 | 3.53 | < .001 | 0.00 | 1052.23 | 0.00 |
Each model is compared to MINT_CaCo.
preds_sex$p
mods_sex <- compare_models_glm("Sex")
display(mods_sex$table, title="Sex")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.16 | 944.58 | |||
| m_metamint | 37.74 | 3.43 | < .001 | 0.10 | 937.32 |
| m_minimint | 0.008 | 4.01 | < .001 | 0.08 | 954.29 |
| m_micromint | 0.349 | 3.76 | < .001 | 0.09 | 946.68 |
| m_maia | < 0.001 | 2.59 | 0.005 | 0.09 | 980.74 |
| m_imaia | < 0.001 | 5.47 | < .001 | 0.01 | 1007.96 |
| m_ias | < 0.001 | 5.91 | < .001 | 0.00 | 998.49 |
| m_bpq | < 0.001 | 5.80 | < .001 | 0.00 | 996.33 |
Each model is compared to m_mint.
mods_sex$p
preds_bmi <- compare_predictors("BMI")
display(preds_bmi$table, title="BMI")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Sati | 0.03 | 4873.64 | -0.89 | |||
| MAIA_NotWorrying | < 0.001 | 1.75 | 0.040 | 0.01 | 4890.73 | 0.44 |
| BPQ | < 0.001 | 2.09 | 0.018 | 0.00 | 4894.04 | -0.17 |
| IAS | < 0.001 | 1.59 | 0.056 | 0.01 | 4889.49 | 0.06 |
Each model is compared to MINT_Sati.
preds_bmi$p
mods_bmi <- compare_models_lm("BMI")
display(mods_bmi, title="BMI")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.05 | 4922.00 | |||
| m_metamint | > 1000 | 2.34 | 0.010 | 0.02 | 4894.07 |
| m_minimint | > 1000 | 2.52 | 0.006 | 0.01 | 4899.62 |
| m_micromint | > 1000 | 2.30 | 0.011 | 0.02 | 4893.35 |
| m_maia | 0.002 | 2.40 | 0.008 | 0.01 | 4934.85 |
| m_imaia | > 1000 | 2.78 | 0.003 | 0.00 | 4906.31 |
| m_ias | > 1000 | 2.60 | 0.005 | 0.01 | 4889.49 |
| m_bpq | > 1000 | 2.90 | 0.002 | 0.00 | 4894.04 |
Each model is compared to m_mint.
preds_physactiv <- compare_predictors("Physical_Active")
display(preds_physactiv$table, title="Physical Activity")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_RelA | 0.05 | 2911.58 | 0.32 | |||
| MAIA_Trusting | > 1000 | -3.70 | < .001 | 0.13 | 2844.29 | 0.47 |
| BPQ | < 0.001 | 2.89 | 0.002 | 0.00 | 2946.74 | 0.01 |
| IAS | < 0.001 | 1.96 | 0.025 | 0.02 | 2932.91 | 0.02 |
Each model is compared to MINT_RelA.
preds_physactiv$p
mods_physactiv <- compare_models_lm("Physical_Active")
display(mods_physactiv, title="Physical Activity")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.09 | 2940.44 | |||
| m_metamint | > 1000 | 2.44 | 0.007 | 0.06 | 2911.50 |
| m_minimint | > 1000 | 2.12 | 0.017 | 0.07 | 2906.06 |
| m_micromint | > 1000 | 2.15 | 0.016 | 0.07 | 2907.55 |
| m_maia | > 1000 | -2.23 | 0.013 | 0.15 | 2876.67 |
| m_imaia | > 1000 | 1.20 | 0.116 | 0.06 | 2910.64 |
| m_ias | 43.15 | 3.99 | < .001 | 0.02 | 2932.91 |
| m_bpq | 0.043 | 4.38 | < .001 | 0.00 | 2946.74 |
Each model is compared to m_mint.
preds_wearN <- compare_predictors("Wearables_Number", family = "poisson")
display(preds_wearN$table, title="Wearables Number")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_SexS | 0.02 | 2969.56 | 0.08 | |||
| MAIA_SelfRegulation | 1.06 | -0.01 | 0.496 | 0.02 | 2969.43 | 0.07 |
| BPQ | 0.003 | 1.16 | 0.123 | 0.00 | 2980.92 | -0.01 |
| IAS | 0.005 | 1.17 | 0.122 | 0.00 | 2979.99 | 0.00 |
Each model is compared to MINT_SexS.
preds_wearN$p
df$Wearables_HeartOwn <- ifelse(df$Wearables_Heart == "Not owning", 0, 1)
preds_wearHeartOwn <- compare_predictors("Wearables_HeartOwn")
display(preds_wearHeartOwn$table, title="Wearables Heart Ownership")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Resp | 0.01 | 957.73 | 0.24 | |||
| MAIA_SelfRegulation | 15.00 | -0.60 | 0.274 | 0.02 | 952.31 | 0.31 |
| BPQ | 0.012 | 1.19 | 0.117 | 0.00 | 966.60 | -0.08 |
| IAS | 0.026 | 1.15 | 0.126 | 0.00 | 964.99 | 0.13 |
Each model is compared to MINT_Resp.
preds_wearHeartOwn$p
mods_wearHeartOwn <- compare_models_glm("Wearables_HeartOwn")
display(mods_wearHeartOwn$table, title="Wearables Heart Ownership")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.03 | 1012.46 | |||
| m_metamint | > 1000 | 1.70 | 0.045 | 0.01 | 971.82 |
| m_minimint | > 1000 | 1.71 | 0.043 | 0.01 | 971.79 |
| m_micromint | > 1000 | 1.26 | 0.104 | 0.02 | 966.04 |
| m_maia | > 1000 | 0.09 | 0.464 | 0.03 | 993.65 |
| m_imaia | > 1000 | 1.42 | 0.078 | 0.01 | 972.21 |
| m_ias | > 1000 | 2.09 | 0.018 | 0.00 | 964.99 |
| m_bpq | > 1000 | 1.99 | 0.023 | 0.00 | 966.60 |
Each model is compared to m_mint.
mods_wearHeartOwn$p
preds_wearHeartImp <- compare_predictors("Wearables_HeartImportance")
display(preds_wearHeartImp$table, title="Wearables Heart Importance")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Card | 0.10 | 1013.08 | 0.37 | |||
| MAIA_BodyListening | 115.97 | -0.64 | 0.262 | 0.13 | 1003.57 | 0.52 |
| BPQ | < 0.001 | 2.52 | 0.006 | 0.01 | 1036.81 | 0.20 |
| IAS | < 0.001 | 1.36 | 0.086 | 0.04 | 1027.99 | 0.04 |
Each model is compared to MINT_Card.
preds_wearHeartImp$p
mods_wearHeartImp <- compare_models_lm("Wearables_HeartImportance")
display(mods_wearHeartImp, title="Wearables Heart Importance")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.19 | 1041.78 | |||
| m_metamint | > 1000 | 2.00 | 0.023 | 0.12 | 1017.22 |
| m_minimint | > 1000 | 2.23 | 0.013 | 0.10 | 1022.77 |
| m_micromint | 10.58 | 2.90 | 0.002 | 0.05 | 1037.06 |
| m_maia | 889.20 | 0.19 | 0.423 | 0.18 | 1028.20 |
| m_imaia | > 1000 | 0.80 | 0.212 | 0.15 | 1009.32 |
| m_ias | 987.72 | 3.14 | < .001 | 0.04 | 1027.99 |
| m_bpq | 11.98 | 3.49 | < .001 | 0.01 | 1036.81 |
Each model is compared to m_mint.
df$Wearables_SleepOwn <- ifelse(df$Wearables_Sleep == "Not owning", 0, 1)
preds_wearSleepOwn <- compare_predictors("Wearables_SleepOwn")
display(preds_wearSleepOwn$table, title="Wearables Sleep Ownership")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Olfa | 0.00 | 806.35 | 0.15 | |||
| MAIA_SelfRegulation | 1.99 | -0.26 | 0.398 | 0.01 | 804.98 | 0.18 |
| BPQ | 0.235 | 0.84 | 0.201 | 0.00 | 809.25 | 0.01 |
| IAS | 0.269 | 0.73 | 0.232 | 0.00 | 808.98 | 0.05 |
Each model is compared to MINT_Olfa.
preds_wearSleepOwn$p
mods_wearSleepOwn <- compare_models_glm("Wearables_SleepOwn")
display(mods_wearSleepOwn$table, title="Wearables Sleep Ownership")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.01 | 865.18 | |||
| m_metamint | > 1000 | 1.48 | 0.069 | 0.00 | 821.27 |
| m_minimint | > 1000 | 1.52 | 0.065 | 0.00 | 821.72 |
| m_micromint | > 1000 | 1.44 | 0.074 | 0.00 | 821.21 |
| m_maia | > 1000 | 0.06 | 0.478 | 0.01 | 845.88 |
| m_imaia | > 1000 | 0.95 | 0.170 | 0.00 | 819.45 |
| m_ias | > 1000 | 1.53 | 0.063 | 0.00 | 808.98 |
| m_bpq | > 1000 | 1.56 | 0.059 | 0.00 | 809.25 |
Each model is compared to m_mint.
preds_wearSleepImp <- compare_predictors("Wearables_SleepImportance")
display(preds_wearSleepImp$table, title="Wearables Sleep Importance")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_RelA | 0.05 | 707.28 | 0.33 | |||
| MAIA_NotWorrying | 0.932 | 0.02 | 0.493 | 0.05 | 707.42 | -0.35 |
| BPQ | 0.121 | 0.56 | 0.287 | 0.02 | 711.51 | 0.33 |
| IAS | 0.038 | 1.10 | 0.135 | 0.01 | 713.81 | 0.02 |
Each model is compared to MINT_RelA.
preds_wearSleepImp$p
mods_wearSleepImp <- compare_models_lm("Wearables_SleepImportance")
display(mods_wearSleepImp, title="Wearables Sleep Importance")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.11 | 747.22 | |||
| m_metamint | > 1000 | 1.37 | 0.086 | 0.07 | 714.19 |
| m_minimint | > 1000 | 1.67 | 0.047 | 0.05 | 717.03 |
| m_micromint | > 1000 | 2.01 | 0.022 | 0.03 | 720.99 |
| m_maia | > 1000 | 0.09 | 0.463 | 0.11 | 732.76 |
| m_imaia | > 1000 | 1.42 | 0.077 | 0.05 | 716.83 |
| m_ias | > 1000 | 1.99 | 0.023 | 0.01 | 713.81 |
| m_bpq | > 1000 | 1.75 | 0.040 | 0.02 | 711.51 |
Each model is compared to m_mint.
df$Wearables_StepsOwn <- ifelse(df$Wearables_Steps == "Not owning", 0, 1)
preds_wearStepsOwn <- compare_predictors("Wearables_StepsOwn")
display(preds_wearStepsOwn$table, title="Wearables Steps Ownership")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_ExaC | 0.01 | 1030.45 | -0.16 | |||
| MAIA_Noticing | 0.406 | 0.38 | 0.351 | 0.00 | 1032.25 | -0.12 |
| BPQ | 0.257 | 0.58 | 0.282 | 0.00 | 1033.16 | -0.10 |
| IAS | 0.222 | 0.86 | 0.195 | 0.00 | 1033.45 | -0.09 |
Each model is compared to MINT_ExaC.
preds_wearStepsOwn$p
mods_wearStepsOwn <- compare_models_glm("Wearables_StepsOwn")
display(mods_wearStepsOwn$table, title="Wearables Steps Ownership")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.01 | 1090.82 | |||
| m_metamint | > 1000 | 1.42 | 0.078 | 0.00 | 1046.01 |
| m_minimint | > 1000 | 1.16 | 0.123 | 0.01 | 1043.38 |
| m_micromint | > 1000 | 1.16 | 0.123 | 0.01 | 1043.39 |
| m_maia | > 1000 | 0.29 | 0.385 | 0.01 | 1073.43 |
| m_imaia | > 1000 | 1.00 | 0.159 | 0.00 | 1045.00 |
| m_ias | > 1000 | 1.42 | 0.078 | 0.00 | 1033.45 |
| m_bpq | > 1000 | 1.24 | 0.108 | 0.00 | 1033.16 |
Each model is compared to m_mint.
preds_wearStepsImp <- compare_predictors("Wearables_StepsImportance")
display(preds_wearStepsImp$table, title="Wearables Steps Importance")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Awareness | 0.07 | 1477.11 | 0.60 | |||
| MAIA_Noticing | 5.68 | -0.26 | 0.399 | 0.08 | 1473.63 | 0.50 |
| BPQ | < 0.001 | 1.63 | 0.052 | 0.02 | 1495.95 | 0.26 |
| IAS | 0.017 | 0.81 | 0.209 | 0.05 | 1485.31 | 0.04 |
Each model is compared to MINT_Awareness.
preds_wearStepsImp$p
mods_wearStepsImp <- compare_models_lm("Wearables_StepsImportance")
display(mods_wearStepsImp, title="Wearables Steps Importance")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.15 | 1501.87 | |||
| m_metamint | > 1000 | 2.28 | 0.011 | 0.10 | 1476.89 |
| m_minimint | > 1000 | 2.49 | 0.006 | 0.09 | 1481.29 |
| m_micromint | 521.34 | 2.85 | 0.002 | 0.07 | 1489.35 |
| m_maia | 0.601 | 1.33 | 0.091 | 0.11 | 1502.88 |
| m_imaia | > 1000 | 1.68 | 0.047 | 0.09 | 1478.59 |
| m_ias | > 1000 | 3.07 | 0.001 | 0.05 | 1485.31 |
| m_bpq | 19.31 | 3.71 | < .001 | 0.02 | 1495.95 |
Each model is compared to m_mint.
df$Wearables_WeightOwn <- ifelse(df$Wearables_Weight == "Not owning", 0, 1)
preds_wearWeightOwn <- compare_predictors("Wearables_WeightOwn")
display(preds_wearWeightOwn$table, title="Wearables Weight Ownership")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_SexS | 0.02 | 988.73 | 0.27 | |||
| MAIA_NotWorrying | 0.011 | 1.18 | 0.120 | 0.00 | 997.67 | 0.12 |
| BPQ | 0.003 | 1.68 | 0.047 | 0.00 | 1000.20 | 0.03 |
| IAS | 0.003 | 1.71 | 0.044 | 0.00 | 1000.26 | 0.02 |
Each model is compared to MINT_SexS.
preds_wearWeightOwn$p
mods_wearWeightOwn <- compare_models_glm("Wearables_WeightOwn")
display(mods_wearWeightOwn$table, title="Wearables Weight Ownership")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.04 | 1034.90 | |||
| m_metamint | > 1000 | 2.66 | 0.004 | 0.00 | 1010.43 |
| m_minimint | > 1000 | 2.80 | 0.003 | 0.00 | 1013.45 |
| m_micromint | > 1000 | 2.61 | 0.005 | 0.01 | 1009.67 |
| m_maia | 0.178 | 1.87 | 0.031 | 0.01 | 1038.35 |
| m_imaia | > 1000 | 2.40 | 0.008 | 0.01 | 1009.74 |
| m_ias | > 1000 | 2.82 | 0.002 | 0.00 | 1000.26 |
| m_bpq | > 1000 | 2.80 | 0.003 | 0.00 | 1000.20 |
Each model is compared to m_mint.
preds_wearWeightImp <- compare_predictors("Wearables_WeightImportance")
display(preds_wearWeightImp$table, title="Wearables Weight Importance")| Name | BF | LR | p (LR) | R2 | BIC | Coefficient |
|---|---|---|---|---|---|---|
| MINT_Awareness | 0.02 | 1076.27 | 0.27 | |||
| MAIA_Noticing | 12.79 | -0.71 | 0.239 | 0.04 | 1071.17 | 0.29 |
| BPQ | 0.066 | 1.09 | 0.138 | 0.00 | 1081.69 | 0.05 |
| IAS | 12.84 | -0.92 | 0.180 | 0.04 | 1071.16 | 0.03 |
Each model is compared to MINT_Awareness.
preds_wearWeightImp$p
mods_wearWeightImp <- compare_models_lm("Wearables_WeightImportance")
display(mods_wearWeightImp, title="Wearables Weight Importance")| Name | BF | LR | p (LR) | R2 | BIC |
|---|---|---|---|---|---|
| m_mint | 0.06 | 1120.93 | |||
| m_metamint | > 1000 | 1.45 | 0.073 | 0.03 | 1083.56 |
| m_minimint | > 1000 | 1.28 | 0.100 | 0.04 | 1082.22 |
| m_micromint | > 1000 | 1.61 | 0.053 | 0.03 | 1084.71 |
| m_maia | > 1000 | -0.38 | 0.350 | 0.07 | 1100.16 |
| m_imaia | > 1000 | 0.52 | 0.301 | 0.04 | 1080.23 |
| m_ias | > 1000 | 0.80 | 0.213 | 0.04 | 1071.16 |
| m_bpq | > 1000 | 2.16 | 0.015 | 0.00 | 1081.69 |
Each model is compared to m_mint.
make_table1 <- function(preds=preds_dif, mods=mods_dif, group = "Alexithymia", outcome = "TAS - DIF") {
yvar <- preds$outcome
preds <- preds$table
best_pred_mint <- preds[str_detect(preds$Name, "MINT"), ]
best_pred_nonmint <- preds[!str_detect(preds$Name, "MINT"), ]
best_pred_nonmint <- best_pred_nonmint[best_pred_nonmint$R2 == max(best_pred_nonmint$R2), ][1,]
best_models_R2 <- str_remove(arrange(mods, desc(as.numeric(R2)))$Name, "m_")
best_model_BIC <- str_remove(arrange(mods, as.numeric(BIC))$Name, "m_")
if(length(unique(df[[yvar]])) > 2) {
# LMs - display correlation
r_mint <- cor.test(cbind(df, metamint)[[best_pred_mint$Name[1]]], df[[yvar]])
r_mint <- paste0(insight::format_value(r_mint$estimate, zap_small = TRUE))
r_nonmint <- cor.test(df[[best_pred_nonmint$Name[1]]], df[[yvar]])
r_nonmint <- paste0(insight::format_value(r_nonmint$estimate, zap_small = TRUE))
} else {
# GLMs - display log-odds
r_mint <- best_pred_mint$Coefficient
r_nonmint <- best_pred_nonmint$Coefficient
}
# Format
best_models_R2[str_detect(best_models_R2, "mint")] <- paste0("<b>", best_models_R2[str_detect(best_models_R2, "mint")], "</b>")
best_model_BIC[str_detect(best_model_BIC, "mint")] <- paste0("<b>", best_model_BIC[str_detect(best_model_BIC, "mint")], "</b>")
best_models_R2 <- paste0("<small>", paste0(best_models_R2, collapse = " > "), "</small>")
best_model_BIC <- paste0("<small>", paste0(best_model_BIC, collapse = " > "), "</small>")
data.frame(
Group = group,
Outcome = outcome,
Best_MINT_Predictor = paste0(str_remove(best_pred_mint$Name[1], "MINT_"), "<br><small><i>(", r_mint, ")</i></small>"),
Best_NonMINT_Predictor = paste0(str_replace(best_pred_nonmint$Name[1], "_", " - "), "<br><small><i>(", r_nonmint, ")</i></small>"),
Best_Models_R2 = best_models_R2,
Best_Models_BIC = best_model_BIC
)
}
rez <- rbind(
make_table1(preds_dif, mods_dif, group = "Alexithymia", outcome = "Difficulty Identifying Feelings (DIF)<small><p align='right'><i>TAS-20</i></small><p>"),
make_table1(preds_ddf, mods_ddf, group = "Alexithymia", outcome = "Difficulty Describing Feelings (DDF)<small><p align='right'><i>TAS-20</i></small><p>"),
make_table1(preds_eot, mods_eot, group = "Alexithymia", outcome = "Externally Oriented Thinking (EOT)<small><p align='right'><i>TAS-20</i></small><p>"),
make_table1(preds_arou, mods_arou, group = "Emotion Reactivity", outcome = "Arousal<small><p align='right'><i>ERS</i></small><p>"),
make_table1(preds_sens, mods_sens, group = "Emotion Reactivity", outcome = "Sensitivity<small><p align='right'><i>ERS</i></small><p>"),
make_table1(preds_pers, mods_pers, group = "Emotion Reactivity", outcome = "Persistence<small><p align='right'><i>ERS</i></small><p>"),
make_table1(preds_ls, mods_ls, group = "Mood", outcome = "Life Satisfaction"),
make_table1(preds_anx, mods_anx, group = "Mood", outcome = "Anxiety<small><p align='right'><i>PHQ-4</i></small><p>"),
make_table1(preds_dep, mods_dep, group = "Mood", outcome = "Depression<small><p align='right'><i>PHQ-4</i></small><p>"),
make_table1(preds_moodt, mods_moodt$table, group = "Mental Health", outcome = "Mood Disorder"),
make_table1(preds_adhd, mods_adhd$table, group = "Mental Health", outcome = "ADHD"),
make_table1(preds_asd, mods_asd$table, group = "Mental Health", outcome = "Autism"),
make_table1(preds_afferent, mods_afferent$table, group = "Somatic Health", outcome = "Afferent Sensitivity<small><small><p align='right'><i>Migraine, neuropathy, muscle tension, dizziness, ...</i></small></small><p>"),
make_table1(preds_central, mods_central$table, group = "Somatic Health", outcome = "Central Sensitization<small><small><p align='right'><i>Fibromyalgia, chronic fatigue, back pain, IBS, ...</i></small></small><p>"),
make_table1(preds_autonomic, mods_autonomic$table, group = "Somatic Health", outcome = "Autonomic Dysfunction<small><small><p align='right'><i>Hypermobility, chest pain, hypo/hypertension, ...</i></small></small><p>"),
make_table1(preds_immune, mods_immune$table, group = "Somatic Health", outcome = "Immune-Inflammatory<small><small><p align='right'><i>Allergies, eczema, autoimmune, ...</i></small></small><p>"),
make_table1(preds_cefsabody, mods_cefsabody, group = "Dissociative Symptoms", outcome = "Body<small><p align='right'><i>CEFSA</i></small><p>"),
make_table1(preds_cefsaself, mods_cefsaself, group = "Dissociative Symptoms", outcome = "Self<small><p align='right'><i>CEFSA</i></small><p>"),
make_table1(preds_cefsaemo, mods_cefsaemo, group = "Dissociative Symptoms", outcome = "Emotions<small><p align='right'><i>CEFSA</i></small><p>"),
make_table1(preds_cefsareal, mods_cefsareal, group = "Dissociative Symptoms", outcome = "Reality<small><p align='right'><i>CEFSA</i></small><p>"),
# make_table1(preds_pialive, mods_pialive, group = "Primals", outcome = "Alive<small><p align='right'><i>PI-18</i></small><p>"),
# make_table1(preds_pisafe, mods_pisafe, group = "Primals", outcome = "Safe<small><p align='right'><i>PI-18</i></small><p>"),
# make_table1(preds_pient, mods_pient, group = "Primals", outcome = "Enticing<small><p align='right'><i>PI-18</i></small><p>"),
# make_table1(preds_pigood, mods_pigood, group = "Primals", outcome = "Good<small><p align='right'><i>PI-18</i></small><p>")
make_table1(preds_bmi, mods_bmi, group = "Lifestyle", outcome = "BMI"),
make_table1(preds_physactiv, mods_physactiv, group = "Lifestyle", outcome = "Physical Activity"),
# make_table1(preds_age, mods_age, group = "Lifestyle", outcome = "Age"),
make_table1(preds_wearHeartImp, mods_wearHeartImp, group = "Lifestyle", outcome = "Cardiac Monitoring"),
make_table1(preds_wearSleepImp, mods_wearSleepImp, group = "Lifestyle", outcome = "Sleep Monitoring"),
make_table1(preds_wearStepsImp, mods_wearStepsImp, group = "Lifestyle", outcome = "Steps Monitoring")
# make_table1(preds_wearWeightImp, mods_wearWeightImp, group = "Lifestyle", outcome = "Weight Monitoring")
)
tab2 <- rez |>
gt::gt() |>
gt::tab_header(
title = gt::md("**Interoceptive Questionnaires Comparison**"),
subtitle = "MINT vs. MAIA, IAS, BPQ"
) |>
gt::cols_width(
Outcome ~ gt::pct(23),
Best_MINT_Predictor ~ gt::pct(12),
Best_NonMINT_Predictor ~ gt::pct(15),
Best_Models_R2 ~ gt::pct(25),
Best_Models_BIC ~ gt::pct(25)
) |>
gt::tab_style(
style = gt::cell_text(weight="bold", v_align = "middle"),
locations = gt::cells_column_labels()
) |>
gt::tab_style(
style = gt::cell_text(align = "center", v_align = "middle"),
locations = gt::cells_body(columns=c("Best_MINT_Predictor", "Best_NonMINT_Predictor"))
) |>
gt::tab_row_group(
label = "Lifestyle",
rows = Group == "Lifestyle"
) |>
gt::tab_row_group(
label = "Dissociative Symptoms",
rows = Group == "Dissociative Symptoms"
) |>
gt::tab_row_group(
label = "Primal World Beliefs",
rows = Group == "Primals"
) |>
gt::tab_row_group(
label = "Somatic Health",
rows = Group == "Somatic Health"
) |>
gt::tab_row_group(
label = "Mental Health",
rows = Group == "Mental Health"
) |>
gt::tab_row_group(
label = "Mood",
rows = Group == "Mood"
) |>
gt::tab_row_group(
label = "Emotional Reactivity",
rows = Group == "Emotion Reactivity"
) |>
gt::tab_row_group(
label = "Alexithymia",
rows = Group == "Alexithymia"
) |>
gt::tab_style(
style = list(
gt::cell_fill(color = "gray85"),
gt::cell_text(weight="bold", style="italic")),
locations = gt::cells_row_groups()
) |>
gt::tab_style(
style = gt::cell_fill(color = "#81C784"),
locations = list(
gt::cells_body(columns="Best_MINT_Predictor", rows=c(1:4, 11:18, 20:21, 24))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#FFB74D"),
locations = list(
gt::cells_body(columns="Best_MINT_Predictor", rows=c(5:9, 10, 19, 22))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#EF9A9A"),
locations = list(
gt::cells_body(columns="Best_MINT_Predictor", rows=c(23, 25))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#81C784"),
locations = list(
gt::cells_body(columns="Best_Models_R2", rows=c(1:3, 11:13, 15:21, 23:25))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#FFB74D"),
locations = list(
gt::cells_body(columns="Best_Models_R2", rows=c(4:10, 14, 22))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#81C784"),
locations = list(
gt::cells_body(columns="Best_Models_BIC", rows=c(1:3, 11:20, 25))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#FFB74D"),
locations = list(
gt::cells_body(columns="Best_Models_BIC", rows=c(4:10, 22))
)
) |>
gt::tab_style(
style = gt::cell_fill(color = "#EF9A9A"),
locations = list(
gt::cells_body(columns="Best_Models_BIC", rows=c(21, 23, 24))
)
) |>
gt::cols_label(
Best_MINT_Predictor = "Best Predictor\n(MINT)",
Best_NonMINT_Predictor = "Best Predictor\n(Non-MINT)",
Best_Models_R2 = "Best Models (R2)",
Best_Models_BIC = "Best Models (BIC)"
) |>
gt::cols_hide(Group) |>
gt::fmt_markdown() |>
gt::tab_options(
table.font.size = gt::pct(125)
)
tab2 |>
gt::tab_options(
table.font.size = gt::pct(50)
) |>
gt::opt_interactive()gt::gtsave(tab2, "figures/table2.png", vwidth=1400, expand=0, quiet=TRUE)fig1a <- patchwork::wrap_elements(
patchwork::wrap_elements(grid::rasterGrob(png::readPNG("figures/fig1a.png"))) +
patchwork::plot_annotation(title = "Exploratory Graphical Analysis (EGA)",
subtitle = "Bootstrapped replication of hierarchical clusters (Methhod = leiden)",
theme = theme(plot.title = element_text(face="bold", hjust = 0, size = rel(1.4)),
plot.subtitle = element_text(hjust = 0, size = rel(1)))))
fig1b <- patchwork::wrap_elements(
patchwork::wrap_elements(grid::rasterGrob(png::readPNG("figures/fig1b.png"))) +
patchwork::plot_annotation(title = "Hierarchical Clustering",
subtitle = "Method = Correlation",
theme = theme(plot.title = element_text(face="bold", hjust = 0, size = rel(1.4)),
plot.subtitle = element_text(hjust = 0, size = rel(1)))))
ggsave("figures/fig1.png", fig1a / fig1b, width=8, height=12, dpi=300)
p_cor <- df |>
select(starts_with("TAS_"), LifeSatisfaction, starts_with("PHQ4_"), starts_with("CEFSA"), starts_with("PI"), starts_with("ERS_")) |>
mutate(LifeSatisfaction = datawizard::reverse_scale(LifeSatisfaction),
PI_Safe = datawizard::reverse_scale(PI_Safe),
PI_Good = datawizard::reverse_scale(PI_Good),
PI_Enticing = datawizard::reverse_scale(PI_Enticing)) |>
rename(`Life Satisfaction*` = LifeSatisfaction,
`Primals - Alive` = PI_Alive,
`Primals - Safe*` = PI_Safe,
`Primals - Good*` = PI_Good,
`Primals - Enticing*` = PI_Enticing) |>
make_cor(cbind(mint, metamint, select(df, starts_with("MAIA"), IAS, BPQ))) +
labs(title="Correlates of Interoception")
p_cor
fig2 <- (p_cormint | p_corintero) / p_cor + plot_layout(heights = c(0.4, 0.6))
ggsave("figures/fig2.png", fig2, width=10, height=12, dpi=300)
fig3 <- (mods_moodt$p + theme(axis.text.x = element_blank(), axis.title.x = element_blank()) |
mods_adhd$p + theme(axis.text.x = element_blank(), axis.title.x = element_blank()) |
mods_asd$p + theme(axis.text.x = element_blank(), axis.title.x = element_blank())) /
(mods_central$p | mods_autonomic$p | mods_immune$p) +
plot_layout(guides = "collect") +
plot_annotation(title = "Predictive Performance of Interoceptive Questionnaires",
subtitle = "ROC Curves",
theme = theme(plot.title = element_text(face="bold", hjust = 0.5, size = rel(1.4)),
plot.subtitle = element_text(hjust = 0.5, size = rel(1.25))))
fig3
ggsave("figures/fig3.png", fig3, width=13, height=9, dpi=300)